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stringlengths 15
1.12k
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stringclasses 79
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values | json
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RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Eruptive epidermoid cysts resulting from treatment with imiquimod .
|
Entities:
- ENTITY: Eruptive epidermoid cysts
- ENTITY: imiquimod
Relations:
- adverse effect: Eruptive epidermoid cysts → imiquimod
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "Eruptive epidermoid cysts", "relation": "adverse effect", "tail": "imiquimod"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Bronchiolitis obliterans organising pneumonia associated with the use of nitrofurantoin .
|
Entities:
- ENTITY: Bronchiolitis obliterans organising pneumonia
- ENTITY: nitrofurantoin
Relations:
- adverse effect: Bronchiolitis obliterans organising pneumonia → nitrofurantoin
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "Bronchiolitis obliterans organising pneumonia", "relation": "adverse effect", "tail": "nitrofurantoin"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
We report the case of a young healthy woman who presented an early overanticoagulation when receiving acenocoumarol for a first thromboembolic episode .
|
Entities:
- ENTITY: acenocoumarol
- ENTITY: overanticoagulation
Relations:
- adverse effect: overanticoagulation → acenocoumarol
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "overanticoagulation", "relation": "adverse effect", "tail": "acenocoumarol"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Hypersensitivity to aspirin can be manifested as acute asthma , urticaria and/or angioedema , or a systemic anaphylactoid reaction .
|
Entities:
- ENTITY: angioedema
- ENTITY: aspirin
- ENTITY: systemic anaphylactoid reaction
- ENTITY: urticaria
Relations:
- adverse effect: angioedema → aspirin
- adverse effect: systemic anaphylactoid reaction → aspirin
- adverse effect: urticaria → aspirin
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "angioedema", "relation": "adverse effect", "tail": "aspirin"}, {"head": "systemic anaphylactoid reaction", "relation": "adverse effect", "tail": "aspirin"}, {"head": "urticaria", "relation": "adverse effect", "tail": "aspirin"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Prick tests and intradermal tests with a series of dilutions of carboplatin and cisplatin were performed on three patients who had exhibited medium and severe hypersensitivity reactions to carboplatin .
|
Entities:
- ENTITY: carboplatin
- ENTITY: severe hypersensitivity
Relations:
- adverse effect: severe hypersensitivity → carboplatin
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "severe hypersensitivity", "relation": "adverse effect", "tail": "carboplatin"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Although the data indicate an immune - complex cause for gold - salt nephropathy , the incident antigen ( or antigens ) and mechanism of action remain unidentified .
|
Entities:
- ENTITY: gold - salt
- ENTITY: nephropathy
Relations:
- adverse effect: nephropathy → gold - salt
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "nephropathy", "relation": "adverse effect", "tail": "gold - salt"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Phenylpropanolamine ( PPA ) recently has been publicly implicated as a cause of stroke and other neurologic events .
|
Entities:
- ENTITY: PPA
- ENTITY: Phenylpropanolamine
- ENTITY: neurologic events
- ENTITY: stroke
Relations:
- adverse effect: neurologic events → Phenylpropanolamine
- adverse effect: neurologic events → PPA
- adverse effect: stroke → Phenylpropanolamine
- adverse effect: stroke → PPA
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "neurologic events", "relation": "adverse effect", "tail": "Phenylpropanolamine"}, {"head": "neurologic events", "relation": "adverse effect", "tail": "PPA"}, {"head": "stroke", "relation": "adverse effect", "tail": "Phenylpropanolamine"}, {"head": "stroke", "relation": "adverse effect", "tail": "PPA"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Multiple complications of propylthiouracil treatment : granulocytopenia , eosinophilia , skin reaction and hepatitis with lymphocyte sensitization .
|
Entities:
- ENTITY: eosinophilia
- ENTITY: granulocytopenia
- ENTITY: hepatitis
- ENTITY: lymphocyte sensitization
- ENTITY: propylthiouracil
- ENTITY: skin reaction
Relations:
- adverse effect: eosinophilia → propylthiouracil
- adverse effect: granulocytopenia → propylthiouracil
- adverse effect: hepatitis → propylthiouracil
- adverse effect: lymphocyte sensitization → propylthiouracil
- adverse effect: skin reaction → propylthiouracil
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "eosinophilia", "relation": "adverse effect", "tail": "propylthiouracil"}, {"head": "granulocytopenia", "relation": "adverse effect", "tail": "propylthiouracil"}, {"head": "hepatitis", "relation": "adverse effect", "tail": "propylthiouracil"}, {"head": "lymphocyte sensitization", "relation": "adverse effect", "tail": "propylthiouracil"}, {"head": "skin reaction", "relation": "adverse effect", "tail": "propylthiouracil"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Hence , hyperthyroidism induced by IFN - alpha could correspond to the first phase of silent thyroiditis , to Graves ' disease or to the succession of both .
|
Entities:
- ENTITY: IFN - alpha
- ENTITY: hyperthyroidism
Relations:
- adverse effect: hyperthyroidism → IFN - alpha
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "hyperthyroidism", "relation": "adverse effect", "tail": "IFN - alpha"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
This regimen could prove useful for other patients who develop hypersensitivity reactions to carboplatin and allow therapy to continue .
|
Entities:
- ENTITY: carboplatin
- ENTITY: hypersensitivity
Relations:
- adverse effect: hypersensitivity → carboplatin
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "hypersensitivity", "relation": "adverse effect", "tail": "carboplatin"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Thus , an immunological mechanism might be involved in the mechanism of pirmenol - induced QT prolongation and T wave inversion on the electrocardiogram .
|
Entities:
- ENTITY: QT prolongation
- ENTITY: T wave inversion
- ENTITY: pirmenol
Relations:
- adverse effect: QT prolongation → pirmenol
- adverse effect: T wave inversion → pirmenol
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "QT prolongation", "relation": "adverse effect", "tail": "pirmenol"}, {"head": "T wave inversion", "relation": "adverse effect", "tail": "pirmenol"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Magnesium tocolysis as the cause of urinary calculus during pregnancy .
|
Entities:
- ENTITY: Magnesium
- ENTITY: urinary calculus
Relations:
- adverse effect: urinary calculus → Magnesium
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "urinary calculus", "relation": "adverse effect", "tail": "Magnesium"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
A case of tuberculosis in a patient on Efalizumab and Etanercept for treatment of refractory palmopustular psoriasis and psoriatic arthritis .
|
Entities:
- ENTITY: Efalizumab
- ENTITY: Etanercept
- ENTITY: tuberculosis
Relations:
- adverse effect: tuberculosis → Efalizumab
- adverse effect: tuberculosis → Etanercept
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "tuberculosis", "relation": "adverse effect", "tail": "Efalizumab"}, {"head": "tuberculosis", "relation": "adverse effect", "tail": "Etanercept"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
OBJECTIVES : The authors described a case of Hashimoto 's disease during interferon - alpha ( IFN - alpha ) treatment for chronic viral C hepatitis in a patient with the specific genetic susceptibility associated with the thyroid disease .
|
Entities:
- ENTITY: Hashimoto 's disease
- ENTITY: IFN - alpha
- ENTITY: interferon - alpha
Relations:
- adverse effect: Hashimoto 's disease → IFN - alpha
- adverse effect: Hashimoto 's disease → interferon - alpha
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "Hashimoto 's disease", "relation": "adverse effect", "tail": "IFN - alpha"}, {"head": "Hashimoto 's disease", "relation": "adverse effect", "tail": "interferon - alpha"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Hepatic damage after danazol treatment .
|
Entities:
- ENTITY: Hepatic damage
- ENTITY: danazol
Relations:
- adverse effect: Hepatic damage → danazol
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "Hepatic damage", "relation": "adverse effect", "tail": "danazol"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Niacin maculopathy .
|
Entities:
- ENTITY: Niacin
- ENTITY: maculopathy
Relations:
- adverse effect: maculopathy → Niacin
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "maculopathy", "relation": "adverse effect", "tail": "Niacin"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Angioedema and dysphagia caused by contact allergy to inhaled budesonide .
|
Entities:
- ENTITY: Angioedema
- ENTITY: budesonide
- ENTITY: dysphagia
Relations:
- adverse effect: Angioedema → budesonide
- adverse effect: dysphagia → budesonide
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "Angioedema", "relation": "adverse effect", "tail": "budesonide"}, {"head": "dysphagia", "relation": "adverse effect", "tail": "budesonide"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
OBSERVATIONS : A 48-year - old woman presented with disfiguring facial edema 10 weeks after she began antiviral therapy with peginterferon alfa-2a and ribavirin for chronic hepatitis C infection .
|
Entities:
- ENTITY: disfiguring facial edema
- ENTITY: peginterferon alfa-2a
- ENTITY: ribavirin
Relations:
- adverse effect: disfiguring facial edema → peginterferon alfa-2a
- adverse effect: disfiguring facial edema → ribavirin
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "disfiguring facial edema", "relation": "adverse effect", "tail": "peginterferon alfa-2a"}, {"head": "disfiguring facial edema", "relation": "adverse effect", "tail": "ribavirin"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
A case report is presented concerning the administration of ketanserin in the treatment of pulmonary vasoconstriction and right ventricular failure following the infusion of protamine in a patient undergoing coronary artery bypass surgery and mitral valve replacement .
|
Entities:
- ENTITY: protamine
- ENTITY: pulmonary vasoconstriction
- ENTITY: right ventricular failure
Relations:
- adverse effect: pulmonary vasoconstriction → protamine
- adverse effect: right ventricular failure → protamine
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "pulmonary vasoconstriction", "relation": "adverse effect", "tail": "protamine"}, {"head": "right ventricular failure", "relation": "adverse effect", "tail": "protamine"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Fatal pulmonary fibrosis associated with BCNU : the relative role of platelet - derived growth factor - B , insulin - like growth factor I , transforming growth factor - beta1 and cyclooxygenase-2 .
|
Entities:
- ENTITY: BCNU
- ENTITY: Fatal pulmonary fibrosis
Relations:
- adverse effect: Fatal pulmonary fibrosis → BCNU
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "Fatal pulmonary fibrosis", "relation": "adverse effect", "tail": "BCNU"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Syncope in a 65-year - old woman after nitrate ingestion .
|
Entities:
- ENTITY: Syncope
- ENTITY: nitrate
Relations:
- adverse effect: Syncope → nitrate
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "Syncope", "relation": "adverse effect", "tail": "nitrate"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
A 57-year - old man developed morphea while taking bromocriptine .
|
Entities:
- ENTITY: bromocriptine
- ENTITY: morphea
Relations:
- adverse effect: morphea → bromocriptine
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "morphea", "relation": "adverse effect", "tail": "bromocriptine"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Hydrocortisone may decrease the incidence of mortality associated with cardiac arrhythmias in children receiving amphotericin B overdoses .
|
Entities:
- ENTITY: amphotericin B
- ENTITY: cardiac arrhythmias
- ENTITY: mortality
Relations:
- adverse effect: cardiac arrhythmias → amphotericin B
- adverse effect: mortality → amphotericin B
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "cardiac arrhythmias", "relation": "adverse effect", "tail": "amphotericin B"}, {"head": "mortality", "relation": "adverse effect", "tail": "amphotericin B"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Clinicians who manage cachectic patients , particularly those with protracted diarrhoea and/or receiving anti - malarial drugs including mefloquine , should be aware of the risk of severe hypoglycaemia .
|
Entities:
- ENTITY: mefloquine
- ENTITY: severe hypoglycaemia
Relations:
- adverse effect: severe hypoglycaemia → mefloquine
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "severe hypoglycaemia", "relation": "adverse effect", "tail": "mefloquine"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
MATERIALS AND METHODS : We present two cases of significant morbidity related to primary and secondary perforation of the bladder following two instillations of epirubicin .
|
Entities:
- ENTITY: epirubicin
- ENTITY: primary and secondary perforation of the bladder
Relations:
- adverse effect: primary and secondary perforation of the bladder → epirubicin
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "primary and secondary perforation of the bladder", "relation": "adverse effect", "tail": "epirubicin"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Gold nephropathy : tissue analysis by X - ray fluorescent spectroscopy .
|
Entities:
- ENTITY: Gold
- ENTITY: nephropathy
Relations:
- adverse effect: nephropathy → Gold
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "nephropathy", "relation": "adverse effect", "tail": "Gold"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Triazolam - induced nocturnal bingeing with amnesia .
|
Entities:
- ENTITY: Triazolam
- ENTITY: amnesia
- ENTITY: nocturnal bingeing
Relations:
- adverse effect: amnesia → Triazolam
- adverse effect: nocturnal bingeing → Triazolam
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "amnesia", "relation": "adverse effect", "tail": "Triazolam"}, {"head": "nocturnal bingeing", "relation": "adverse effect", "tail": "Triazolam"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
This case presentation is of a patient who had the clinical appearance of epiglottitis , but actually had an oro - pharyngeal dystonic reaction to prochlorperazine .
|
Entities:
- ENTITY: epiglottitis
- ENTITY: oro - pharyngeal dystonic reaction
- ENTITY: prochlorperazine
Relations:
- adverse effect: epiglottitis → prochlorperazine
- adverse effect: oro - pharyngeal dystonic reaction → prochlorperazine
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "epiglottitis", "relation": "adverse effect", "tail": "prochlorperazine"}, {"head": "oro - pharyngeal dystonic reaction", "relation": "adverse effect", "tail": "prochlorperazine"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
The probable proarrhythmic action of amiodarone , although rare , is reviewed along with a discussion of the novel use of intravenous magnesium sulfate therapy .
|
Entities:
- ENTITY: amiodarone
- ENTITY: proarrhythmic
Relations:
- adverse effect: proarrhythmic → amiodarone
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "proarrhythmic", "relation": "adverse effect", "tail": "amiodarone"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Five and one - half years after the diagnosis of myeloma , while in remission on cyclophosphamide therapy , the patient experienced severe abdominal right lower quadrant pain due to a large cecal lymphoma .
|
Entities:
- ENTITY: abdominal right lower quadrant pain
- ENTITY: cecal lymphoma
- ENTITY: cyclophosphamide
Relations:
- adverse effect: abdominal right lower quadrant pain → cyclophosphamide
- adverse effect: cecal lymphoma → cyclophosphamide
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "abdominal right lower quadrant pain", "relation": "adverse effect", "tail": "cyclophosphamide"}, {"head": "cecal lymphoma", "relation": "adverse effect", "tail": "cyclophosphamide"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
OBJECTIVE : To report a case of cutaneous and hematologic toxicity in a patient treated with IL-2 .
|
Entities:
- ENTITY: IL-2
- ENTITY: cutaneous and hematologic toxicity
Relations:
- adverse effect: cutaneous and hematologic toxicity → IL-2
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "cutaneous and hematologic toxicity", "relation": "adverse effect", "tail": "IL-2"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Sulfasalazine - induced lupus erythematosus .
|
Entities:
- ENTITY: Sulfasalazine
- ENTITY: lupus erythematosus
Relations:
- adverse effect: lupus erythematosus → Sulfasalazine
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "lupus erythematosus", "relation": "adverse effect", "tail": "Sulfasalazine"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Nephrotic syndrome associated with interferon - beta-1b therapy for multiple sclerosis .
|
Entities:
- ENTITY: Nephrotic syndrome
- ENTITY: interferon - beta-1b
Relations:
- adverse effect: Nephrotic syndrome → interferon - beta-1b
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "Nephrotic syndrome", "relation": "adverse effect", "tail": "interferon - beta-1b"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
We report for the first time the development of symptomatic methemoglobinemia after an acute ingestion of divalproex sodium ( Depakote ) , resulting in serum concentrations 10 times greater than the therapeutic range .
|
Entities:
- ENTITY: Depakote
- ENTITY: divalproex sodium
- ENTITY: symptomatic methemoglobinemia
Relations:
- adverse effect: symptomatic methemoglobinemia → Depakote
- adverse effect: symptomatic methemoglobinemia → divalproex sodium
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "symptomatic methemoglobinemia", "relation": "adverse effect", "tail": "Depakote"}, {"head": "symptomatic methemoglobinemia", "relation": "adverse effect", "tail": "divalproex sodium"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
CONCLUSIONS : In our reported case , a local hyperproduction of TNF - alpha from macrophages that was induced by the injected insulin could explain the dedifferentiation of the adipocytes of the subcutaneous tissue and the reversion that was induced by the local injection of dexamethasone .
|
Entities:
- ENTITY: dedifferentiation of the adipocytes
- ENTITY: hyperproduction of TNF - alpha
- ENTITY: insulin
Relations:
- adverse effect: dedifferentiation of the adipocytes → insulin
- adverse effect: hyperproduction of TNF - alpha → insulin
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "dedifferentiation of the adipocytes", "relation": "adverse effect", "tail": "insulin"}, {"head": "hyperproduction of TNF - alpha", "relation": "adverse effect", "tail": "insulin"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Renal injury due to anastrozole has not been published in the English literature .
|
Entities:
- ENTITY: Renal injury
- ENTITY: anastrozole
Relations:
- adverse effect: Renal injury → anastrozole
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "Renal injury", "relation": "adverse effect", "tail": "anastrozole"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Sustained - release procainamide - induced reversible granulocytopenia after myocardial infarction .
|
Entities:
- ENTITY: procainamide
- ENTITY: reversible granulocytopenia
Relations:
- adverse effect: reversible granulocytopenia → procainamide
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "reversible granulocytopenia", "relation": "adverse effect", "tail": "procainamide"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
During clarithromycin coadministration , four out of the seven patients developed moderate - to - severe toxic symptoms of carbamazepine , such as drowsiness , dizziness , and ataxia , which resolved within 5 days after clarithromycin discontinuation .
|
Entities:
- ENTITY: ataxia
- ENTITY: carbamazepine
- ENTITY: clarithromycin
- ENTITY: dizziness
- ENTITY: drowsiness
- ENTITY: toxic symptoms
Relations:
- adverse effect: ataxia → carbamazepine
- adverse effect: ataxia → clarithromycin
- adverse effect: ataxia → clarithromycin
- adverse effect: dizziness → carbamazepine
- adverse effect: dizziness → clarithromycin
- adverse effect: dizziness → clarithromycin
- adverse effect: drowsiness → carbamazepine
- adverse effect: drowsiness → clarithromycin
- adverse effect: drowsiness → clarithromycin
- adverse effect: toxic symptoms → carbamazepine
- adverse effect: toxic symptoms → clarithromycin
- adverse effect: toxic symptoms → clarithromycin
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "ataxia", "relation": "adverse effect", "tail": "carbamazepine"}, {"head": "ataxia", "relation": "adverse effect", "tail": "clarithromycin"}, {"head": "ataxia", "relation": "adverse effect", "tail": "clarithromycin"}, {"head": "dizziness", "relation": "adverse effect", "tail": "carbamazepine"}, {"head": "dizziness", "relation": "adverse effect", "tail": "clarithromycin"}, {"head": "dizziness", "relation": "adverse effect", "tail": "clarithromycin"}, {"head": "drowsiness", "relation": "adverse effect", "tail": "carbamazepine"}, {"head": "drowsiness", "relation": "adverse effect", "tail": "clarithromycin"}, {"head": "drowsiness", "relation": "adverse effect", "tail": "clarithromycin"}, {"head": "toxic symptoms", "relation": "adverse effect", "tail": "carbamazepine"}, {"head": "toxic symptoms", "relation": "adverse effect", "tail": "clarithromycin"}, {"head": "toxic symptoms", "relation": "adverse effect", "tail": "clarithromycin"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
However , cyclosporine dependency is associated with the risk of nephrotoxicity .
|
Entities:
- ENTITY: cyclosporine
- ENTITY: nephrotoxicity
Relations:
- adverse effect: nephrotoxicity → cyclosporine
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "nephrotoxicity", "relation": "adverse effect", "tail": "cyclosporine"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
We experienced a case of chronic renal failure in a patient suffering from acute hemorrhagic gastritis associated with AZ intoxication .
|
Entities:
- ENTITY: AZ
- ENTITY: AZ intoxication
- ENTITY: acute hemorrhagic gastritis
Relations:
- adverse effect: acute hemorrhagic gastritis → AZ
- adverse effect: AZ intoxication → AZ
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "acute hemorrhagic gastritis", "relation": "adverse effect", "tail": "AZ"}, {"head": "AZ intoxication", "relation": "adverse effect", "tail": "AZ"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
We now report the first known cancer patient who developed life - threatening complications after treatment with topical 5-FU and was shown subsequently to have profound DPD deficiency .
|
Entities:
- ENTITY: 5-FU
- ENTITY: life - threatening complications
Relations:
- adverse effect: life - threatening complications → 5-FU
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "life - threatening complications", "relation": "adverse effect", "tail": "5-FU"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
To develop information on the relative rarity or frequency of neurologic worsening with the initiation of penicillamine therapy , we conducted a retrospective survey of 25 additional patients with Wilson 's disease who met the criteria of presenting with neurologic disease and having been treated with penicillamine .
|
Entities:
- ENTITY: neurologic disease
- ENTITY: neurologic worsening
- ENTITY: penicillamine
Relations:
- adverse effect: neurologic disease → penicillamine
- adverse effect: neurologic worsening → penicillamine
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "neurologic disease", "relation": "adverse effect", "tail": "penicillamine"}, {"head": "neurologic worsening", "relation": "adverse effect", "tail": "penicillamine"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Cephalosporins are most likely associated with Vitamin K deficiency .
|
Entities:
- ENTITY: Cephalosporins
- ENTITY: Vitamin K deficiency
Relations:
- adverse effect: Vitamin K deficiency → Cephalosporins
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "Vitamin K deficiency", "relation": "adverse effect", "tail": "Cephalosporins"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Multiple seizures after bupropion overdose in a small child .
|
Entities:
- ENTITY: Multiple seizures
- ENTITY: bupropion
Relations:
- adverse effect: Multiple seizures → bupropion
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "Multiple seizures", "relation": "adverse effect", "tail": "bupropion"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
To our knowledge this is the first report that demonstrates histological abnormalities of the glomerulus associated with postoperative IFN - beta therapy for the malignant melanoma .
|
Entities:
- ENTITY: IFN - beta
- ENTITY: histological abnormalities of the glomerulus
Relations:
- adverse effect: histological abnormalities of the glomerulus → IFN - beta
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "histological abnormalities of the glomerulus", "relation": "adverse effect", "tail": "IFN - beta"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
BACKGROUND : Reproductive endocrine disorders characterized by menstrual disorders , polycystic ovaries , and hyperandrogenism seem to be common among women treated with sodium valproate for epilepsy .
|
Entities:
- ENTITY: Reproductive endocrine disorders
- ENTITY: hyperandrogenism
- ENTITY: menstrual disorders
- ENTITY: polycystic ovaries
- ENTITY: sodium valproate
Relations:
- adverse effect: hyperandrogenism → sodium valproate
- adverse effect: menstrual disorders → sodium valproate
- adverse effect: polycystic ovaries → sodium valproate
- adverse effect: Reproductive endocrine disorders → sodium valproate
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "hyperandrogenism", "relation": "adverse effect", "tail": "sodium valproate"}, {"head": "menstrual disorders", "relation": "adverse effect", "tail": "sodium valproate"}, {"head": "polycystic ovaries", "relation": "adverse effect", "tail": "sodium valproate"}, {"head": "Reproductive endocrine disorders", "relation": "adverse effect", "tail": "sodium valproate"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
However , in order to avoid neuropathic side effects , patients under thalidomide therapy should be monitored every 6 months with nerve conduction studies while taking the drug .
|
Entities:
- ENTITY: neuropathic side effects
- ENTITY: thalidomide
Relations:
- adverse effect: neuropathic side effects → thalidomide
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "neuropathic side effects", "relation": "adverse effect", "tail": "thalidomide"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Conventional and diffusion - weighted MRI findings of methotrexate related sub - acute neurotoxicity .
|
Entities:
- ENTITY: methotrexate
- ENTITY: sub - acute neurotoxicity
Relations:
- adverse effect: sub - acute neurotoxicity → methotrexate
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "sub - acute neurotoxicity", "relation": "adverse effect", "tail": "methotrexate"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
The clinical course suggested that recombinant alpha-2b peginterferon plus ribavirin provoked type 1 diabetes mellitus , therefore , in patients who are candidates for interferon therapy the presence of pancreatic autoantibodies and the fasting plasma glucose level should be investigated before and during treatment .
|
Entities:
- ENTITY: recombinant alpha-2b peginterferon
- ENTITY: ribavirin
- ENTITY: type 1 diabetes mellitus
Relations:
- adverse effect: type 1 diabetes mellitus → recombinant alpha-2b peginterferon
- adverse effect: type 1 diabetes mellitus → ribavirin
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "type 1 diabetes mellitus", "relation": "adverse effect", "tail": "recombinant alpha-2b peginterferon"}, {"head": "type 1 diabetes mellitus", "relation": "adverse effect", "tail": "ribavirin"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
A case of anaphylactoid reaction due solely to the use of Gelofusine in a patient with non - haemorrhagic hypovolaemia is presented , with a discussion on the management and the use of allergy identification jewellery .
|
Entities:
- ENTITY: Gelofusine
- ENTITY: anaphylactoid reaction
Relations:
- adverse effect: anaphylactoid reaction → Gelofusine
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "anaphylactoid reaction", "relation": "adverse effect", "tail": "Gelofusine"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
SUBSEQUENT COURSE : The nephrosis resolved almost completely once the interferon was stopped and after immunosuppressive treatment .
|
Entities:
- ENTITY: interferon
- ENTITY: nephrosis
Relations:
- adverse effect: nephrosis → interferon
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "nephrosis", "relation": "adverse effect", "tail": "interferon"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
The association of central diabetes insipidus ( CDI ) with lithium use is rare .
|
Entities:
- ENTITY: CDI
- ENTITY: central diabetes insipidus
- ENTITY: lithium
Relations:
- adverse effect: CDI → lithium
- adverse effect: central diabetes insipidus → lithium
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "CDI", "relation": "adverse effect", "tail": "lithium"}, {"head": "central diabetes insipidus", "relation": "adverse effect", "tail": "lithium"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Apparent central nervous system depression in infants after the use of topical brimonidine .
|
Entities:
- ENTITY: brimonidine
- ENTITY: central nervous system depression
Relations:
- adverse effect: central nervous system depression → brimonidine
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "central nervous system depression", "relation": "adverse effect", "tail": "brimonidine"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
The case demonstrates that hypersensitivity reaction to pranlukast and resultant ATIN is possible , and that periodic urine testing in patients receiving pranlukast should be considered .
|
Entities:
- ENTITY: ATIN
- ENTITY: hypersensitivity reaction
- ENTITY: pranlukast
Relations:
- adverse effect: ATIN → pranlukast
- adverse effect: ATIN → pranlukast
- adverse effect: hypersensitivity reaction → pranlukast
- adverse effect: hypersensitivity reaction → pranlukast
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "ATIN", "relation": "adverse effect", "tail": "pranlukast"}, {"head": "ATIN", "relation": "adverse effect", "tail": "pranlukast"}, {"head": "hypersensitivity reaction", "relation": "adverse effect", "tail": "pranlukast"}, {"head": "hypersensitivity reaction", "relation": "adverse effect", "tail": "pranlukast"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Five days after the fourth dose of vincristine , she presented with bilateral ptosis .
|
Entities:
- ENTITY: bilateral ptosis
- ENTITY: vincristine
Relations:
- adverse effect: bilateral ptosis → vincristine
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "bilateral ptosis", "relation": "adverse effect", "tail": "vincristine"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Clozapine induced polyserositis .
|
Entities:
- ENTITY: Clozapine
- ENTITY: polyserositis
Relations:
- adverse effect: polyserositis → Clozapine
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "polyserositis", "relation": "adverse effect", "tail": "Clozapine"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Four days after the initial injection of 3.6 mg of goserelin acetate , severe dyspnea developed due to worsening pleuritis carcinomatosa , which was considered as a flare - up .
|
Entities:
- ENTITY: flare - up
- ENTITY: goserelin acetate
- ENTITY: severe dyspnea
- ENTITY: worsening pleuritis carcinomatosa
Relations:
- adverse effect: flare - up → goserelin acetate
- adverse effect: severe dyspnea → goserelin acetate
- adverse effect: worsening pleuritis carcinomatosa → goserelin acetate
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "flare - up", "relation": "adverse effect", "tail": "goserelin acetate"}, {"head": "severe dyspnea", "relation": "adverse effect", "tail": "goserelin acetate"}, {"head": "worsening pleuritis carcinomatosa", "relation": "adverse effect", "tail": "goserelin acetate"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Second , we report a case of neutropenia , which proved to be fatal in a schizophrenia patient receiving olanzapine and thiazide .
|
Entities:
- ENTITY: neutropenia
- ENTITY: olanzapine
- ENTITY: thiazide
Relations:
- adverse effect: neutropenia → olanzapine
- adverse effect: neutropenia → thiazide
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "neutropenia", "relation": "adverse effect", "tail": "olanzapine"}, {"head": "neutropenia", "relation": "adverse effect", "tail": "thiazide"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
In two patients with mycosis fungoides , a squamous cell carcinoma developed during therapy with psoralens plus long - wave ultraviolet radiation ( PUVA ) .
|
Entities:
- ENTITY: psoralens
- ENTITY: squamous cell carcinoma
Relations:
- adverse effect: squamous cell carcinoma → psoralens
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "squamous cell carcinoma", "relation": "adverse effect", "tail": "psoralens"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Flurbiprofen - associated acute tubulointerstitial nephritis .
|
Entities:
- ENTITY: Flurbiprofen
- ENTITY: acute tubulointerstitial nephritis
Relations:
- adverse effect: acute tubulointerstitial nephritis → Flurbiprofen
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "acute tubulointerstitial nephritis", "relation": "adverse effect", "tail": "Flurbiprofen"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
The mechanism of anaphylactoid reaction to zomepirac in this case , therefore , remains unclear .
|
Entities:
- ENTITY: anaphylactoid reaction
- ENTITY: zomepirac
Relations:
- adverse effect: anaphylactoid reaction → zomepirac
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "anaphylactoid reaction", "relation": "adverse effect", "tail": "zomepirac"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
L - asparaginase - provoked seizures as singular expression of central nervous toxicity .
|
Entities:
- ENTITY: L - asparaginase
- ENTITY: central nervous toxicity
- ENTITY: seizures
Relations:
- adverse effect: central nervous toxicity → L - asparaginase
- adverse effect: seizures → L - asparaginase
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "central nervous toxicity", "relation": "adverse effect", "tail": "L - asparaginase"}, {"head": "seizures", "relation": "adverse effect", "tail": "L - asparaginase"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Possible linkage of amprenavir with intracranial bleeding in an HIV - infected hemophiliac .
|
Entities:
- ENTITY: amprenavir
- ENTITY: intracranial bleeding
Relations:
- adverse effect: intracranial bleeding → amprenavir
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "intracranial bleeding", "relation": "adverse effect", "tail": "amprenavir"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Within 24 hours of fluid restriction and cessation of desmopressin , her symptoms and hyponatremia resolved .
|
Entities:
- ENTITY: desmopressin
- ENTITY: hyponatremia
Relations:
- adverse effect: hyponatremia → desmopressin
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "hyponatremia", "relation": "adverse effect", "tail": "desmopressin"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
A study following large patient groups on theophylline and a combination of theophylline and steroids might clarify the risk of ulcer formation in patients being treated with these medications for asthma .
|
Entities:
- ENTITY: theophylline
- ENTITY: ulcer
Relations:
- adverse effect: ulcer → theophylline
- adverse effect: ulcer → theophylline
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "ulcer", "relation": "adverse effect", "tail": "theophylline"}, {"head": "ulcer", "relation": "adverse effect", "tail": "theophylline"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
High - dose intravenous mannitol infusion in various clinical settings may result in acute renal failure ( ARF ) .
|
Entities:
- ENTITY: ARF
- ENTITY: acute renal failure
- ENTITY: mannitol
Relations:
- adverse effect: acute renal failure → mannitol
- adverse effect: ARF → mannitol
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "acute renal failure", "relation": "adverse effect", "tail": "mannitol"}, {"head": "ARF", "relation": "adverse effect", "tail": "mannitol"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
This report details a case of bilateral avascular necrosis of the femoral heads in a patient receiving ' standard ' doses of dexamethasone as part of the antiemetic regimen used in cisplatin - based combination chemotherapy .
|
Entities:
- ENTITY: bilateral avascular necrosis of the femoral heads
- ENTITY: dexamethasone
Relations:
- adverse effect: bilateral avascular necrosis of the femoral heads → dexamethasone
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "bilateral avascular necrosis of the femoral heads", "relation": "adverse effect", "tail": "dexamethasone"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
CASE SUMMARY : A 46-year - old African - American man experienced recurrent grand mal seizures during intravenous infusion of amphotericin B , then petit mal seizures as the infusion was stopped and the drug concentrations decreased with time .
|
Entities:
- ENTITY: amphotericin B
- ENTITY: grand mal seizures
Relations:
- adverse effect: grand mal seizures → amphotericin B
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "grand mal seizures", "relation": "adverse effect", "tail": "amphotericin B"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Because psoralens sensitize skin to ultraviolet A light , phototoxic reactions are the most frequent adverse effect of this treatment .
|
Entities:
- ENTITY: phototoxic reactions
- ENTITY: psoralens
Relations:
- adverse effect: phototoxic reactions → psoralens
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "phototoxic reactions", "relation": "adverse effect", "tail": "psoralens"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
We report an unusual pattern of supravenous hyperpigmentation occurring after CHOP chemotherapy .
|
Entities:
- ENTITY: CHOP
- ENTITY: supravenous hyperpigmentation
Relations:
- adverse effect: supravenous hyperpigmentation → CHOP
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "supravenous hyperpigmentation", "relation": "adverse effect", "tail": "CHOP"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
The authors present a case study of a mentally healthy man who repeatedly experienced short - lived , obsessional - like suicidal ideas and images after ingestion of the anti - fungal drug ketoconazole .
|
Entities:
- ENTITY: ketoconazole
- ENTITY: obsessional - like suicidal ideas and images
Relations:
- adverse effect: obsessional - like suicidal ideas and images → ketoconazole
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "obsessional - like suicidal ideas and images", "relation": "adverse effect", "tail": "ketoconazole"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
After extensive neurological ' work up ' , we realized that the anisocoria was related to the transdermal scopolamine patch that we had prescribed for weaning off the opioid .
|
Entities:
- ENTITY: anisocoria
- ENTITY: scopolamine
Relations:
- adverse effect: anisocoria → scopolamine
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "anisocoria", "relation": "adverse effect", "tail": "scopolamine"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
We assume that rIFN - gamma induced the de novo development of SLE in our patient .
|
Entities:
- ENTITY: SLE
- ENTITY: rIFN - gamma
Relations:
- adverse effect: SLE → rIFN - gamma
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "SLE", "relation": "adverse effect", "tail": "rIFN - gamma"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Atrial fibrillation following methylprednisolone pulse therapy .
|
Entities:
- ENTITY: Atrial fibrillation
- ENTITY: methylprednisolone
Relations:
- adverse effect: Atrial fibrillation → methylprednisolone
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "Atrial fibrillation", "relation": "adverse effect", "tail": "methylprednisolone"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
One patient with systemic lupus erythematosus developed erythema multiforme after taking griseofulvin .
|
Entities:
- ENTITY: erythema multiforme
- ENTITY: griseofulvin
Relations:
- adverse effect: erythema multiforme → griseofulvin
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "erythema multiforme", "relation": "adverse effect", "tail": "griseofulvin"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
She was receiving phenytoin sodium 300 mg / day ; carbamazepine 200 mg four times daily had been discontinued four days before admission because of leukopenia .
|
Entities:
- ENTITY: carbamazepine
- ENTITY: leukopenia
- ENTITY: phenytoin sodium
Relations:
- adverse effect: leukopenia → carbamazepine
- adverse effect: leukopenia → phenytoin sodium
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "leukopenia", "relation": "adverse effect", "tail": "carbamazepine"}, {"head": "leukopenia", "relation": "adverse effect", "tail": "phenytoin sodium"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
We describe the case of acute hepatitis induced by gliclazide , a second generation sulfonylurea .
|
Entities:
- ENTITY: acute hepatitis
- ENTITY: gliclazide
Relations:
- adverse effect: acute hepatitis → gliclazide
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "acute hepatitis", "relation": "adverse effect", "tail": "gliclazide"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
OBJECTIVE : To report the safe use of fluorouracil in a patient with breast cancer who had allergic reactions to capecitabine .
|
Entities:
- ENTITY: allergic reactions
- ENTITY: capecitabine
Relations:
- adverse effect: allergic reactions → capecitabine
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "allergic reactions", "relation": "adverse effect", "tail": "capecitabine"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
A patient with chronic myeloid leukaemia treated with busulphan for 4 - 5 years , developed signs of busulphan toxicity and portal hypertension with ascites , oesophageal varices and jaundice .
|
Entities:
- ENTITY: ascites
- ENTITY: busulphan
- ENTITY: jaundice
- ENTITY: oesophageal varices
- ENTITY: portal hypertension
Relations:
- adverse effect: ascites → busulphan
- adverse effect: jaundice → busulphan
- adverse effect: oesophageal varices → busulphan
- adverse effect: portal hypertension → busulphan
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "ascites", "relation": "adverse effect", "tail": "busulphan"}, {"head": "jaundice", "relation": "adverse effect", "tail": "busulphan"}, {"head": "oesophageal varices", "relation": "adverse effect", "tail": "busulphan"}, {"head": "portal hypertension", "relation": "adverse effect", "tail": "busulphan"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Niflumic acid - induced skeletal fluorosis : iatrogenic disease or therapeutic perspective for osteoporosis ?
|
Entities:
- ENTITY: Niflumic acid
- ENTITY: skeletal fluorosis
Relations:
- adverse effect: skeletal fluorosis → Niflumic acid
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "skeletal fluorosis", "relation": "adverse effect", "tail": "Niflumic acid"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Hyponatraemia developed after rechallenge with controlled release carbamazepine .
|
Entities:
- ENTITY: Hyponatraemia
- ENTITY: carbamazepine
Relations:
- adverse effect: Hyponatraemia → carbamazepine
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "Hyponatraemia", "relation": "adverse effect", "tail": "carbamazepine"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
There are no previous reports in the literature about the emergence of CML during treatment with hydroxyurea .
|
Entities:
- ENTITY: CML
- ENTITY: hydroxyurea
Relations:
- adverse effect: CML → hydroxyurea
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "CML", "relation": "adverse effect", "tail": "hydroxyurea"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
The 5 patients had severe renovascular disease which might thus represent a significant risk factor in the development of captopril - induced acute renal failure .
|
Entities:
- ENTITY: acute renal failure
- ENTITY: captopril
Relations:
- adverse effect: acute renal failure → captopril
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "acute renal failure", "relation": "adverse effect", "tail": "captopril"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Since 1979 , over 30 published case reports have documented the relationship between phenylpropanolamine and stroke .
|
Entities:
- ENTITY: phenylpropanolamine
- ENTITY: stroke
Relations:
- adverse effect: stroke → phenylpropanolamine
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "stroke", "relation": "adverse effect", "tail": "phenylpropanolamine"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
DISCUSSION : To our knowledge this is the first reported case of tuberculous uveitis following treatment with etanercept .
|
Entities:
- ENTITY: etanercept
- ENTITY: tuberculous uveitis
Relations:
- adverse effect: tuberculous uveitis → etanercept
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "tuberculous uveitis", "relation": "adverse effect", "tail": "etanercept"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
We describe a case of interstitial hypoxaemiant pneumonitis probably related to flecainide in a patient with the LEOPARD syndrome , a rare congenital disorder .
|
Entities:
- ENTITY: flecainide
- ENTITY: interstitial hypoxaemiant pneumonitis
Relations:
- adverse effect: interstitial hypoxaemiant pneumonitis → flecainide
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "interstitial hypoxaemiant pneumonitis", "relation": "adverse effect", "tail": "flecainide"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Methotrexate - induced pneumonitis in patients with rheumatoid arthritis and psoriatic arthritis : report of five cases and review of the literature .
|
Entities:
- ENTITY: Methotrexate
- ENTITY: pneumonitis
Relations:
- adverse effect: pneumonitis → Methotrexate
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "pneumonitis", "relation": "adverse effect", "tail": "Methotrexate"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
While 40 mg / day of prednisolone improved hepatic dysfunction dramatically , her diabetic milieu deteriorated seriously .
|
Entities:
- ENTITY: diabetic milieu deteriorated
- ENTITY: prednisolone
Relations:
- adverse effect: diabetic milieu deteriorated → prednisolone
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "diabetic milieu deteriorated", "relation": "adverse effect", "tail": "prednisolone"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Ulcerating enteritis associated with flucytosine therapy .
|
Entities:
- ENTITY: Ulcerating enteritis
- ENTITY: flucytosine
Relations:
- adverse effect: Ulcerating enteritis → flucytosine
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "Ulcerating enteritis", "relation": "adverse effect", "tail": "flucytosine"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
CONCLUSION : This rare case of PTU - induced ANCA - associated vasculitis manifested with ototoxicity in combination with systemic involvement .
|
Entities:
- ENTITY: ANCA - associated vasculitis
- ENTITY: PTU
- ENTITY: ototoxicity
Relations:
- adverse effect: ANCA - associated vasculitis → PTU
- adverse effect: ototoxicity → PTU
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "ANCA - associated vasculitis", "relation": "adverse effect", "tail": "PTU"}, {"head": "ototoxicity", "relation": "adverse effect", "tail": "PTU"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
We describe two women who developed HUS after MMC therapy and presented massive pulmonary bleeding .
|
Entities:
- ENTITY: HUS
- ENTITY: MMC
- ENTITY: massive pulmonary bleeding
Relations:
- adverse effect: HUS → MMC
- adverse effect: massive pulmonary bleeding → MMC
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "HUS", "relation": "adverse effect", "tail": "MMC"}, {"head": "massive pulmonary bleeding", "relation": "adverse effect", "tail": "MMC"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
CONCLUSION : These cases suggest that moxifloxacin may interfere with the healing of corneal ulcers .
|
Entities:
- ENTITY: corneal ulcers
- ENTITY: moxifloxacin
Relations:
- adverse effect: corneal ulcers → moxifloxacin
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "corneal ulcers", "relation": "adverse effect", "tail": "moxifloxacin"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
CONCLUSION : During and after IFN therapy , OCT is a useful examination technique for revealing macular edema in patients who have decreased vision .
|
Entities:
- ENTITY: IFN
- ENTITY: decreased vision
- ENTITY: macular edema
Relations:
- adverse effect: decreased vision → IFN
- adverse effect: macular edema → IFN
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "decreased vision", "relation": "adverse effect", "tail": "IFN"}, {"head": "macular edema", "relation": "adverse effect", "tail": "IFN"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Although the two local anesthetics usually do not cause methemoglobinemia , we suspect that the displacement of lidocaine from protein binding by bupivacaine , in combination with metabolic acidosis and treatment with other oxidants , was the reason for the development of methemoglobinemia .
|
Entities:
- ENTITY: lidocaine
- ENTITY: methemoglobinemia
Relations:
- adverse effect: methemoglobinemia → lidocaine
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "methemoglobinemia", "relation": "adverse effect", "tail": "lidocaine"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Here we describe another case of VOD occurring after LT , but in which the causative role was played by azathioprine .
|
Entities:
- ENTITY: VOD
- ENTITY: azathioprine
Relations:
- adverse effect: VOD → azathioprine
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "VOD", "relation": "adverse effect", "tail": "azathioprine"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Hypo - oestrogenic and anabolic / androgenic side - effects of danazol are well known by the gynaecologist and some of them are present in > 50 % of patients being treated for endometriosis .
|
Entities:
- ENTITY: Hypo - oestrogenic and anabolic / androgenic side - effects
- ENTITY: danazol
Relations:
- adverse effect: Hypo - oestrogenic and anabolic / androgenic side - effects → danazol
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "Hypo - oestrogenic and anabolic / androgenic side - effects", "relation": "adverse effect", "tail": "danazol"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
The other patient developed transient intraoperative hypertension immediately after inadvertent submucosal injection of concentrated epinephrine .
|
Entities:
- ENTITY: epinephrine
- ENTITY: transient intraoperative hypertension
Relations:
- adverse effect: transient intraoperative hypertension → epinephrine
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "transient intraoperative hypertension", "relation": "adverse effect", "tail": "epinephrine"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Inadvertent and accidental epinephrine overdose might result in potentially lethal complications .
|
Entities:
- ENTITY: epinephrine
- ENTITY: lethal complications
Relations:
- adverse effect: lethal complications → epinephrine
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "lethal complications", "relation": "adverse effect", "tail": "epinephrine"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
Acute pancreatitis is a known , although rare , complication of mesalamine treatment .
|
Entities:
- ENTITY: Acute pancreatitis
- ENTITY: mesalamine
Relations:
- adverse effect: Acute pancreatitis → mesalamine
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "Acute pancreatitis", "relation": "adverse effect", "tail": "mesalamine"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract entities and their semantic relations from the text according to the schema.
|
From these data , acute generalized dystonia with brainstem and thalamic lesions may occur in WD patients after an initial d - penicillamine therapy .
|
Entities:
- ENTITY: acute generalized dystonia
- ENTITY: brainstem and thalamic lesions
- ENTITY: d - penicillamine
Relations:
- adverse effect: acute generalized dystonia → d - penicillamine
- adverse effect: brainstem and thalamic lesions → d - penicillamine
|
league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
wiki_4: member of; New-York-Times-RE: company major shareholders, sports team location of teams; conll04_zeroshot: Kill; wiki_0: league; conll04: Organization based in; kbp37: country of birth; semval-RE: member collection
|
{"relations": [{"head": "acute generalized dystonia", "relation": "adverse effect", "tail": "d - penicillamine"}, {"head": "brainstem and thalamic lesions", "relation": "adverse effect", "tail": "d - penicillamine"}]}
|
You are an expert in Relation Extraction.
Your task is to identify entities and extract semantic relations between them.
Instructions:
1. First identify all relevant entities in the text
2. Then identify semantic relations between entity pairs
3. Each relation connects exactly two entities: head → tail
4. Use only relation types from the provided schema
5. Format your response as shown below
Format:
Entities:
- ENTITY: entity_name
Relations:
- RELATION_TYPE: head_entity → tail_entity
Available Schema: league, member collection, member of, adverse effect, company major shareholders, kill, country of birth, organization based in, sports team location of teams
|
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