task
stringclasses 7
values | dataset
stringclasses 34
values | subset
stringclasses 1
value | instruction
stringclasses 7
values | content
stringlengths 1
8.18k
| output
stringlengths 4
3.74k
| schema
stringclasses 79
values | negative_labels
stringclasses 55
values | json
stringlengths 14
6.84k
| system_prompt
stringclasses 7
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RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
We report a patient who developed acute pancreatitis during amiodarone therapy .
|
adverse effect: acute pancreatitis -> amiodarone
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "acute pancreatitis", "relation": "adverse effect", "tail": "amiodarone"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Acute pancreatitis after long - term 5-aminosalicylic acid therapy .
|
adverse effect: Acute pancreatitis -> 5-aminosalicylic acid
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "Acute pancreatitis", "relation": "adverse effect", "tail": "5-aminosalicylic acid"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
These in vitro findings and clinical course suggest that TRAb / TBII without thyroid - stimulating activity may develop in patients with amiodarone - induced destructive thyroiditis , as reported in patients with destructive thyroiditis , such as subacute and silent thyroiditis .
|
adverse effect: TRAb / TBII without thyroid - stimulating activity -> amiodarone | adverse effect: thyroiditis -> amiodarone | adverse effect: destructive thyroiditis -> amiodarone
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "destructive thyroiditis", "relation": "adverse effect", "tail": "amiodarone"}, {"head": "thyroiditis", "relation": "adverse effect", "tail": "amiodarone"}, {"head": "TRAb / TBII without thyroid - stimulating activity", "relation": "adverse effect", "tail": "amiodarone"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
A 10-year - old boy with osteosarcoma and normal renal function manifested laboratory evidence of impending renal toxicity and extreme elevation of aspartate aminotrasferase and alanine aminotransferase within 2 hours after the completion of a 4-hour infusion of high - dose methotrexate ( MTX ) ( 12 g / m2 ) , and went on to develop acute renal failure with life - threatening hyperkalemia 29 hours later .
|
adverse effect: acute renal failure -> methotrexate | adverse effect: renal toxicity -> MTX | adverse effect: acute renal failure -> MTX | adverse effect: elevation of aspartate aminotrasferase -> methotrexate | adverse effect: renal toxicity -> methotrexate | adverse effect: elevation of aspartate aminotrasferase -> MTX
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "acute renal failure", "relation": "adverse effect", "tail": "methotrexate"}, {"head": "acute renal failure", "relation": "adverse effect", "tail": "MTX"}, {"head": "elevation of aspartate aminotrasferase", "relation": "adverse effect", "tail": "methotrexate"}, {"head": "elevation of aspartate aminotrasferase", "relation": "adverse effect", "tail": "MTX"}, {"head": "renal toxicity", "relation": "adverse effect", "tail": "methotrexate"}, {"head": "renal toxicity", "relation": "adverse effect", "tail": "MTX"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Described here are 2 patients who developed thrombotic microangiopathy of the kidneys after receiving high cumulative doses of the new anticancer drug gemcitabine .
|
adverse effect: thrombotic microangiopathy -> gemcitabine
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "thrombotic microangiopathy", "relation": "adverse effect", "tail": "gemcitabine"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
We describe a 41 yr old leprosy patient treated for 10 yrs with clofazimine who underwent laparotomy for severe abdominal pain .
|
adverse effect: abdominal pain -> clofazimine
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "abdominal pain", "relation": "adverse effect", "tail": "clofazimine"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
We have seen a case of terminal malignant melanoma in which clinical manifestations , indicative of anterior spinal artery syndrome , developed following the injection of 0.3 ml of 10 % phenol - glycerine into the cervical subarachnoid space at the C4 - -C5 level for the control of severe right arm pain .
|
adverse effect: anterior spinal artery syndrome -> phenol - glycerine
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "anterior spinal artery syndrome", "relation": "adverse effect", "tail": "phenol - glycerine"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Temsirolimus - induced glomerulopathy .
|
adverse effect: glomerulopathy -> Temsirolimus
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "glomerulopathy", "relation": "adverse effect", "tail": "Temsirolimus"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
We describe rupture of a cerebral arterial aneurysm in a 32 year old hypertensive woman following the introduction of nifedipine treatment .
|
adverse effect: rupture of a cerebral arterial aneurysm -> nifedipine
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "rupture of a cerebral arterial aneurysm", "relation": "adverse effect", "tail": "nifedipine"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
DLST is a good diagnostic tool for AZA allergy , especially for severe drug allergy cases .
|
adverse effect: allergy -> AZA
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "allergy", "relation": "adverse effect", "tail": "AZA"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
These novel findings may offer specific therapeutic targets in the treatment of BCNU - associated pulmonary fibrosis .
|
adverse effect: pulmonary fibrosis -> BCNU
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "pulmonary fibrosis", "relation": "adverse effect", "tail": "BCNU"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
We present a case of stroke after PPA ingestion that occurred 4 months after the recall in an 8-year - old boy on chronic peritoneal dialysis .
|
adverse effect: stroke -> PPA
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "stroke", "relation": "adverse effect", "tail": "PPA"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
We report a case of AILD in an 80-year - old male who presented with a generalized pruritic maculopapular eruption and fever following doxycycline administration .
|
adverse effect: fever -> doxycycline | adverse effect: AILD -> doxycycline | adverse effect: generalized pruritic maculopapular eruption -> doxycycline
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "AILD", "relation": "adverse effect", "tail": "doxycycline"}, {"head": "fever", "relation": "adverse effect", "tail": "doxycycline"}, {"head": "generalized pruritic maculopapular eruption", "relation": "adverse effect", "tail": "doxycycline"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
The male patient was treated with 225-mg / day clozapine and the time to the diagnosis of agranulocytosis was 6 weeks .
|
adverse effect: agranulocytosis -> clozapine
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "agranulocytosis", "relation": "adverse effect", "tail": "clozapine"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Unintended exposure to acyclovir early in pregnancy , which is not uncommon , may cause excessive maternal and physician anxiety .
|
adverse effect: excessive maternal and physician anxiety -> acyclovir
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "excessive maternal and physician anxiety", "relation": "adverse effect", "tail": "acyclovir"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Optic neuropathy developed in a patient with rheumatoid arthritis who had been receiving D - penicillamine for about 1 year .
|
adverse effect: Optic neuropathy -> D - penicillamine
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "Optic neuropathy", "relation": "adverse effect", "tail": "D - penicillamine"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
A 73 year - old patient with Wolff - Parkinson - White syndrome and paroxysmic supraventricular tachycardia developed an acute reversible encephalopathy within 15 days of initiation of flecainide .
|
adverse effect: acute reversible encephalopathy -> flecainide
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "acute reversible encephalopathy", "relation": "adverse effect", "tail": "flecainide"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Neurointensive care management of raised intracranial pressure caused by severe valproic acid intoxication .
|
adverse effect: raised intracranial pressure -> valproic acid
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "raised intracranial pressure", "relation": "adverse effect", "tail": "valproic acid"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
The patient 's other comorbidities and medications have not been suggested as possible interactions with sertraline that can cause rhabdomyolysis .
|
adverse effect: rhabdomyolysis -> sertraline
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "rhabdomyolysis", "relation": "adverse effect", "tail": "sertraline"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Osteonecrosis is a serious side effect of antiemetic treatment with dexamethasone and this serious complication should be incorporated in the current guidelines .
|
adverse effect: Osteonecrosis -> dexamethasone
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "Osteonecrosis", "relation": "adverse effect", "tail": "dexamethasone"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
A drug interaction between zafirlukast and theophylline .
|
adverse effect: drug interaction -> theophylline | adverse effect: drug interaction -> zafirlukast
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "drug interaction", "relation": "adverse effect", "tail": "theophylline"}, {"head": "drug interaction", "relation": "adverse effect", "tail": "zafirlukast"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Sulfasalazine - induced lung disorder is an extremely rare entity which must be considered in all ulcerative colitis patients while on sulfasalazine therapy , despite the absence of pulmonary symptomatology .
|
adverse effect: ulcerative colitis -> Sulfasalazine | adverse effect: lung disorder -> Sulfasalazine | adverse effect: ulcerative colitis -> sulfasalazine
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "lung disorder", "relation": "adverse effect", "tail": "Sulfasalazine"}, {"head": "ulcerative colitis", "relation": "adverse effect", "tail": "Sulfasalazine"}, {"head": "ulcerative colitis", "relation": "adverse effect", "tail": "sulfasalazine"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
In this report , we present a case of hypoglycaemic coma associated with SP , an adverse reaction that is likely to be underreported and expected to occur with greater frequency as the use of SP increases .
|
adverse effect: hypoglycaemic coma -> SP
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "hypoglycaemic coma", "relation": "adverse effect", "tail": "SP"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
A 66-year - old mand suffering from severe coronary heart disease took digoxin with suicidal intent an was treated for the ensuing complete atrioventricular block with digoxin - specific antibody fragments .
|
adverse effect: complete atrioventricular block -> digoxin
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "complete atrioventricular block", "relation": "adverse effect", "tail": "digoxin"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
A 15-year follow - up of phenytoin - induced unilateral gingival hyperplasia : a case report .
|
adverse effect: unilateral gingival hyperplasia -> phenytoin
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "unilateral gingival hyperplasia", "relation": "adverse effect", "tail": "phenytoin"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
We report a case of successful surgical management of arterial thrombosis after percutaneous thrombin injection of a femoral artery pseudoaneurysm in a 69-year - old woman .
|
adverse effect: arterial thrombosis -> thrombin
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "arterial thrombosis", "relation": "adverse effect", "tail": "thrombin"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
We document the abrupt development of an extensive choroidal detachment after initiation of dorzolamide therapy in a surgically untreated eye with primary open - angle glaucoma .
|
adverse effect: choroidal detachment -> dorzolamide
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "choroidal detachment", "relation": "adverse effect", "tail": "dorzolamide"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
We report on three cases wherein treatment of dexmedetomidine - induced bradycardia with i.v . glycopyrrolate ( 5.0 microg / kg ) not only resulting in resolution of bradycardia but also resulting in an exaggerated increase of arterial blood pressure .
|
adverse effect: exaggerated increase of arterial blood pressure -> glycopyrrolate | adverse effect: bradycardia -> dexmedetomidine
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "bradycardia", "relation": "adverse effect", "tail": "dexmedetomidine"}, {"head": "exaggerated increase of arterial blood pressure", "relation": "adverse effect", "tail": "glycopyrrolate"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Trimethoprim - sulfamethoxazole - induced hepatotoxicity in a pediatric patient .
|
adverse effect: hepatotoxicity -> Trimethoprim - sulfamethoxazole
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "hepatotoxicity", "relation": "adverse effect", "tail": "Trimethoprim - sulfamethoxazole"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
He had hypokalemia ( K 2.3 mmol / L ) induced by licorice and also had received disopyramide for arrhythmia , bicalutamide for prostate cancer , and silodosin for prostate hypertrophy .
|
adverse effect: hypokalemia -> licorice
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "hypokalemia", "relation": "adverse effect", "tail": "licorice"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Early peritoneal dialysis has not previously been reported for lisinopril induced multiorgan failure .
|
adverse effect: multiorgan failure -> lisinopril
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "multiorgan failure", "relation": "adverse effect", "tail": "lisinopril"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
This case report describes a patient who was previously prescribed alendronate ( Fosamax ) and presented with postoperative hypophosphatemia and hypocalcemic tetany after bowel preparation with Fleet Phospho - Soda .
|
adverse effect: hypophosphatemia -> alendronate | adverse effect: hypocalcemic tetany -> Fosamax | adverse effect: hypophosphatemia -> Fosamax | adverse effect: hypocalcemic tetany -> alendronate | adverse effect: hypocalcemic tetany -> Fleet Phospho - Soda | adverse effect: hypophosphatemia -> Fleet Phospho - Soda
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "hypocalcemic tetany", "relation": "adverse effect", "tail": "alendronate"}, {"head": "hypocalcemic tetany", "relation": "adverse effect", "tail": "Fleet Phospho - Soda"}, {"head": "hypocalcemic tetany", "relation": "adverse effect", "tail": "Fosamax"}, {"head": "hypophosphatemia", "relation": "adverse effect", "tail": "alendronate"}, {"head": "hypophosphatemia", "relation": "adverse effect", "tail": "Fleet Phospho - Soda"}, {"head": "hypophosphatemia", "relation": "adverse effect", "tail": "Fosamax"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
A brief review of reported cases of chloramphenicol hypersensitivity in the English - language literature , as well as possible alternative explanations in this case , are provided .
|
adverse effect: hypersensitivity -> chloramphenicol
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "hypersensitivity", "relation": "adverse effect", "tail": "chloramphenicol"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Refractory hypoglycemia from ciprofloxacin and glyburide interaction .
|
adverse effect: Refractory hypoglycemia -> glyburide | adverse effect: Refractory hypoglycemia -> ciprofloxacin
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "Refractory hypoglycemia", "relation": "adverse effect", "tail": "ciprofloxacin"}, {"head": "Refractory hypoglycemia", "relation": "adverse effect", "tail": "glyburide"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Rupture of a cerebral aneurysm associated with nifedipine treatment .
|
adverse effect: Rupture of a cerebral aneurysm -> nifedipine
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "Rupture of a cerebral aneurysm", "relation": "adverse effect", "tail": "nifedipine"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
An unusual case of Ecstasy poisoning .
|
adverse effect: Ecstasy poisoning -> Ecstasy
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "Ecstasy poisoning", "relation": "adverse effect", "tail": "Ecstasy"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
It is presumed that her hyperchloremic metabolic acidosis was secondary to cholestyramine because of the similarity to pediatric reports ; the rapid and lasting response to intravenous sodium bicarbonate ; the absence of another etiology ; normal serum potassium , chloride and bicarbonate despite continued spironolactone therapy after recovery .
|
adverse effect: hyperchloremic metabolic acidosis -> cholestyramine
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "hyperchloremic metabolic acidosis", "relation": "adverse effect", "tail": "cholestyramine"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
A patient developed optic neuropathy while being treated with isoniazid and ethambutol .
|
adverse effect: optic neuropathy -> isoniazid | adverse effect: optic neuropathy -> ethambutol
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "optic neuropathy", "relation": "adverse effect", "tail": "ethambutol"}, {"head": "optic neuropathy", "relation": "adverse effect", "tail": "isoniazid"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
CONCLUSIONS : The piloerection observed after the replacement of fluvoxamine with milnacipran in this patient appears to have been due to an increase in the alpha(1 ) - adrenoceptor occupancy by endogenous norepinephrine induced by milnacipran .
|
adverse effect: piloerection -> fluvoxamine | adverse effect: piloerection -> milnacipran
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "piloerection", "relation": "adverse effect", "tail": "fluvoxamine"}, {"head": "piloerection", "relation": "adverse effect", "tail": "milnacipran"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Four years after the beginning of IFN therapy , he acutely developed moderate hyperglycemia and severe ketonuria with positive islet cell antibody , and then 28 units / day of insulin injection was started .
|
adverse effect: severe ketonuria -> IFN | adverse effect: moderate hyperglycemia -> IFN
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "moderate hyperglycemia", "relation": "adverse effect", "tail": "IFN"}, {"head": "severe ketonuria", "relation": "adverse effect", "tail": "IFN"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
A 65-year - old woman with bipolar disorder and complicated cardiovascular disease who was on maintenance lithium therapy developed a movement disorder following high doses of trazodone for treatment of an acute depression .
|
adverse effect: movement disorder -> trazodone | adverse effect: movement disorder -> lithium
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "movement disorder", "relation": "adverse effect", "tail": "lithium"}, {"head": "movement disorder", "relation": "adverse effect", "tail": "trazodone"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
OBJECTIVE : We report a patient who developed neutropenia on clozapine , but behind the cell count decrease showed to be a diurnal variation of the white blood cells ( WBC ) .
|
adverse effect: diurnal variation of the white blood cells -> clozapine
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "diurnal variation of the white blood cells", "relation": "adverse effect", "tail": "clozapine"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Therefore , we diagnosed her eruption as contact dermatitis due to sodium bisulfite .
|
adverse effect: eruption -> sodium bisulfite | adverse effect: contact dermatitis -> sodium bisulfite
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "contact dermatitis", "relation": "adverse effect", "tail": "sodium bisulfite"}, {"head": "eruption", "relation": "adverse effect", "tail": "sodium bisulfite"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
After the chlorambucil was discontinued , the wbc count began to slowly rise and the patient developed clinical AML .
|
adverse effect: AML -> chlorambucil
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "AML", "relation": "adverse effect", "tail": "chlorambucil"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Disopyramide - induced heart block .
|
adverse effect: heart block -> Disopyramide
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "heart block", "relation": "adverse effect", "tail": "Disopyramide"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
CASE SUMMARY : A 57-year - old female with cardiomyopathy and " sulfa " ( trimethoprim / sulfamethoxazole ) allergy documented as pancreatitis presented with symptoms consistent with pancreatitis after use of furosemide .
|
adverse effect: pancreatitis -> furosemide
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "pancreatitis", "relation": "adverse effect", "tail": "furosemide"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
To our knowledge , the syndrome of fever , pulmonary infiltrates , and pleural effusion following use of acyclovir has not been previously reported .
|
adverse effect: pleural effusion -> acyclovir | adverse effect: pulmonary infiltrates -> acyclovir | adverse effect: fever -> acyclovir
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "fever", "relation": "adverse effect", "tail": "acyclovir"}, {"head": "pleural effusion", "relation": "adverse effect", "tail": "acyclovir"}, {"head": "pulmonary infiltrates", "relation": "adverse effect", "tail": "acyclovir"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Albuterol - induced hypokalemia and its potential cardiac toxicity are discussed briefly .
|
adverse effect: cardiac toxicity -> Albuterol | adverse effect: hypokalemia -> Albuterol
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "cardiac toxicity", "relation": "adverse effect", "tail": "Albuterol"}, {"head": "hypokalemia", "relation": "adverse effect", "tail": "Albuterol"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Severe hepatotoxicity related to benzarone : a report of three cases with two fatalities .
|
adverse effect: Severe hepatotoxicity -> benzarone
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "Severe hepatotoxicity", "relation": "adverse effect", "tail": "benzarone"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Use of the Naranjo adverse drug reaction probability scale indicated a probable relationship ( score of 5 ) between the patient 's development of hepatotoxicity and the TMP - SMX therapy .
|
adverse effect: hepatotoxicity -> TMP - SMX
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "hepatotoxicity", "relation": "adverse effect", "tail": "TMP - SMX"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
CASE SUMMARIES : Two patients with stable hypothyroidism experienced symptoms of hypothyroidism with increased serum thyroid - stimulating hormone ( TSH ) concentrations after switching from 1 levothyroxine product to another .
|
adverse effect: increased serum thyroid - stimulating hormone -> levothyroxine
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "increased serum thyroid - stimulating hormone", "relation": "adverse effect", "tail": "levothyroxine"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Case 4 : A 61-year - old male alcoholic who remained completely abstinent while taking cyanamide for 3 years showed slight elevation of serum transaminases .
|
adverse effect: elevation of serum transaminases -> cyanamide
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "elevation of serum transaminases", "relation": "adverse effect", "tail": "cyanamide"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
We describe 2 children with cerebral palsy who suffered significant morbidity immediately after treatment with hyperbaric oxygen .
|
adverse effect: morbidity -> hyperbaric oxygen
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "morbidity", "relation": "adverse effect", "tail": "hyperbaric oxygen"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Stupor and fast activity on electroencephalography in a child treated with valproate .
|
adverse effect: Stupor -> valproate
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "Stupor", "relation": "adverse effect", "tail": "valproate"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
The events of non - convulsive status epilepticus subsided following reduction in tiagabine dosages .
|
adverse effect: non - convulsive status epilepticus -> tiagabine
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "non - convulsive status epilepticus", "relation": "adverse effect", "tail": "tiagabine"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
This entity is probably related to a combination of high doses of corticosteroids , vecuronium administration and metabolic abnormalities associated with respiratory failure .
|
adverse effect: respiratory failure -> vecuronium
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "respiratory failure", "relation": "adverse effect", "tail": "vecuronium"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Upper tract urothelial malignancy after cyclophosphamide therapy : a case report and literature review .
|
adverse effect: Upper tract urothelial malignancy -> cyclophosphamide
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "Upper tract urothelial malignancy", "relation": "adverse effect", "tail": "cyclophosphamide"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Rhabdomyolysis associated with the use of intravenous vasopressin .
|
adverse effect: Rhabdomyolysis -> vasopressin
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "Rhabdomyolysis", "relation": "adverse effect", "tail": "vasopressin"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
We describe a case of an NHL patient who received rituximab and developed symptomatic , biopsy - proven multinodular bronchiolitis obliterans with organizing pneumonia ( BOOP ) .
|
adverse effect: bronchiolitis obliterans with organizing pneumonia -> rituximab
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "bronchiolitis obliterans with organizing pneumonia", "relation": "adverse effect", "tail": "rituximab"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
RESULTS : Both patients experienced a previously unreported side effect -- falling backward -- associated with bupropion use .
|
adverse effect: falling backward -> bupropion
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "falling backward", "relation": "adverse effect", "tail": "bupropion"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
OBJECTIVE : To report a case of linear immunoglobulin ( Ig ) A bullous dermatosis ( LABD ) induced by gemcitabine .
|
adverse effect: linear immunoglobulin ( Ig ) A bullous dermatosis -> gemcitabine | adverse effect: LABD -> gemcitabine
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "LABD", "relation": "adverse effect", "tail": "gemcitabine"}, {"head": "linear immunoglobulin ( Ig ) A bullous dermatosis", "relation": "adverse effect", "tail": "gemcitabine"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
To the best of our knowledge , this is the first reported patient with captopril - induced pemphigus in whom no new lesions developed after subsequent treatment with enalapril .
|
adverse effect: pemphigus -> captopril
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "pemphigus", "relation": "adverse effect", "tail": "captopril"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
We present findings from three patients who experienced a psoriasiform eruption apparently due to the antiepileptic agents sodium valproate and carbamazepine .
|
adverse effect: psoriasiform eruption -> sodium valproate | adverse effect: psoriasiform eruption -> carbamazepine
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "psoriasiform eruption", "relation": "adverse effect", "tail": "carbamazepine"}, {"head": "psoriasiform eruption", "relation": "adverse effect", "tail": "sodium valproate"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
In our patient , DIAN possibly was related to cefuroxime , but the patient did not experience associated allergic symptoms .
|
adverse effect: DIAN -> cefuroxime
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "DIAN", "relation": "adverse effect", "tail": "cefuroxime"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Thus , the patient 's clinical course and workup strongly support a diagnosis of lenalidomide - induced hypersensitivity pneumonitis - like syndrome .
|
adverse effect: hypersensitivity pneumonitis - like syndrome -> lenalidomide
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "hypersensitivity pneumonitis - like syndrome", "relation": "adverse effect", "tail": "lenalidomide"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Within 3 weeks of beginning continuous daily isoniazid and rifampin therapy for pulmonary tuberculosis , a patient developed acute renal failure .
|
adverse effect: acute renal failure -> isoniazid | adverse effect: acute renal failure -> rifampin
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "acute renal failure", "relation": "adverse effect", "tail": "isoniazid"}, {"head": "acute renal failure", "relation": "adverse effect", "tail": "rifampin"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
CONCLUSIONS : These results suggest that clozapine may cause TD ; however , the prevalence is low and the severity is relatively mild , with no or mild self - reported discomfort .
|
adverse effect: discomfort -> clozapine | adverse effect: TD -> clozapine
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "discomfort", "relation": "adverse effect", "tail": "clozapine"}, {"head": "TD", "relation": "adverse effect", "tail": "clozapine"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
We observed transient panhypogammaglobulinaemia in a patient with neuropsychiatric SLE after treatment with prednisolone and cyclophosphamide .
|
adverse effect: panhypogammaglobulinaemia -> prednisolone | adverse effect: panhypogammaglobulinaemia -> cyclophosphamide
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "panhypogammaglobulinaemia", "relation": "adverse effect", "tail": "cyclophosphamide"}, {"head": "panhypogammaglobulinaemia", "relation": "adverse effect", "tail": "prednisolone"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Central nervous system manifestations of an ibuprofen overdose reversed by naloxone .
|
adverse effect: Central nervous system manifestations -> ibuprofen
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "Central nervous system manifestations", "relation": "adverse effect", "tail": "ibuprofen"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Generalised pustular psoriasis induced by cyclosporin a withdrawal responding to the tumour necrosis factor alpha inhibitor etanercept .
|
adverse effect: Generalised pustular psoriasis -> cyclosporin a
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "Generalised pustular psoriasis", "relation": "adverse effect", "tail": "cyclosporin a"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
We describe children and adolescents with chronic hematologic and oncologic diseases who exhibited drug - seeking behavior or anticholinergic symptoms with the use of diphenhydramine .
|
adverse effect: drug - seeking behavior -> diphenhydramine | adverse effect: anticholinergic symptoms -> diphenhydramine
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "anticholinergic symptoms", "relation": "adverse effect", "tail": "diphenhydramine"}, {"head": "drug - seeking behavior", "relation": "adverse effect", "tail": "diphenhydramine"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Myoclonus was induced and enhanced by L - dopa , developing into generalized seizures .
|
adverse effect: generalized seizures -> L - dopa | adverse effect: Myoclonus -> L - dopa
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "generalized seizures", "relation": "adverse effect", "tail": "L - dopa"}, {"head": "Myoclonus", "relation": "adverse effect", "tail": "L - dopa"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Patients from endemic areas referred to transplant centers may be at high risk for disseminated histoplasmosis when treated with long - term prednisone for graft - versus - host disease .
|
adverse effect: disseminated histoplasmosis -> prednisone
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "disseminated histoplasmosis", "relation": "adverse effect", "tail": "prednisone"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
A 62-year - old Indian with diabetic nephropathy controlled with metformin , developed miliary tuberculosis for which he was treated with rifampicin , isoniazid and ethambutol .
|
adverse effect: miliary tuberculosis -> metformin
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "miliary tuberculosis", "relation": "adverse effect", "tail": "metformin"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
A diagnosis of infliximab - induced lupus was made and the drug treatment was withdrawn .
|
adverse effect: lupus -> infliximab
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "lupus", "relation": "adverse effect", "tail": "infliximab"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
We report the case of a man , treated with mesalazine for Crohn 's disease who developed drug - induced pericarditis .
|
adverse effect: pericarditis -> mesalazine
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "pericarditis", "relation": "adverse effect", "tail": "mesalazine"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
The possible effects of tamoxifen upon the uterus are discussed in this article , in view of reports of tamoxifen associated with endometrial carcinoma and endometriosis .
|
adverse effect: endometrial carcinoma -> tamoxifen | adverse effect: endometriosis -> tamoxifen
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "endometrial carcinoma", "relation": "adverse effect", "tail": "tamoxifen"}, {"head": "endometriosis", "relation": "adverse effect", "tail": "tamoxifen"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Jaundice induced by streptokinase .
|
adverse effect: Jaundice -> streptokinase
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "Jaundice", "relation": "adverse effect", "tail": "streptokinase"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
This fourth type of cutaneous minocycline hyperpigmentation may be a variant of Type I , but based on clinical , pathological and microanalytical differences , appears to be a new entity .
|
adverse effect: hyperpigmentation -> minocycline
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "hyperpigmentation", "relation": "adverse effect", "tail": "minocycline"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
We describe a patient with the antiphospholipid syndrome who had skin necrosis develop from low - molecular weight heparin therapy at sites distant from injection sites .
|
adverse effect: skin necrosis -> low - molecular weight heparin
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "skin necrosis", "relation": "adverse effect", "tail": "low - molecular weight heparin"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
OBJECTIVE : To report a case of colchicine - induced myopathy in a teenager with familial Mediterranean fever ( FMF ) .
|
adverse effect: myopathy -> colchicine
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "myopathy", "relation": "adverse effect", "tail": "colchicine"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Azathioprine - induced myelosuppression due to thiopurine methyltransferase deficiency in a patient with autoimmune hepatitis .
|
adverse effect: myelosuppression -> Azathioprine
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "myelosuppression", "relation": "adverse effect", "tail": "Azathioprine"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
CONCLUSIONS : Cefazolin was a probable cause of this patient 's leukopenia .
|
adverse effect: leukopenia -> Cefazolin
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "leukopenia", "relation": "adverse effect", "tail": "Cefazolin"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Persisent ocular hypertension following intravitreal ranibizumab .
|
adverse effect: Persisent ocular hypertension -> ranibizumab
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "Persisent ocular hypertension", "relation": "adverse effect", "tail": "ranibizumab"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Serious phenytoin hypersensitivity reactions may appear as dermatologic , lymphoid , or hepatic syndromes .
|
adverse effect: dermatologic , lymphoid , or hepatic syndromes -> phenytoin | adverse effect: hypersensitivity reactions -> phenytoin
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "dermatologic , lymphoid , or hepatic syndromes", "relation": "adverse effect", "tail": "phenytoin"}, {"head": "hypersensitivity reactions", "relation": "adverse effect", "tail": "phenytoin"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Four Chinese female patients who suffered from manic - depressive disorder and underlying autoimmune thyroiditis developed transient episodes of thyrotoxicosis during maintenance lithium therapy .
|
adverse effect: thyrotoxicosis -> lithium
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "thyrotoxicosis", "relation": "adverse effect", "tail": "lithium"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
One of the side - effects of intravitreal triamcinolone is the development of cataract , and it is known that cataract extraction can exacerbate macular degeneration .
|
adverse effect: cataract -> triamcinolone
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "cataract", "relation": "adverse effect", "tail": "triamcinolone"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Although myelosuppression is mild , immunosuppression and superinfection are potential hazards of treatment with DCF .
|
adverse effect: superinfection -> DCF | adverse effect: immunosuppression -> DCF
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "immunosuppression", "relation": "adverse effect", "tail": "DCF"}, {"head": "superinfection", "relation": "adverse effect", "tail": "DCF"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
A 17-year - old female patient who had been taking oral minocycline ( 50 mg twice daily ) for 3 weeks for acne developed an eruption that progressed to an exfoliative dermatitis .
|
adverse effect: exfoliative dermatitis -> minocycline | adverse effect: eruption -> minocycline
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "eruption", "relation": "adverse effect", "tail": "minocycline"}, {"head": "exfoliative dermatitis", "relation": "adverse effect", "tail": "minocycline"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Children receiving zonisamide should be monitored for oligohidrosis and the development of neurological symptoms associated with an elevation of body temperature .
|
adverse effect: oligohidrosis -> zonisamide | adverse effect: neurological symptoms -> zonisamide
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "neurological symptoms", "relation": "adverse effect", "tail": "zonisamide"}, {"head": "oligohidrosis", "relation": "adverse effect", "tail": "zonisamide"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
We report a rare case of colonic mucosal necrosis following Kalimate ( calcium polystryrene sulfonate ) , an analogue of Kayexalate without sorbitol in a 34-yr - old man .
|
adverse effect: colonic mucosal necrosis -> Kalimate | adverse effect: colonic mucosal necrosis -> Kayexalate | adverse effect: colonic mucosal necrosis -> calcium polystryrene sulfonate
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "colonic mucosal necrosis", "relation": "adverse effect", "tail": "calcium polystryrene sulfonate"}, {"head": "colonic mucosal necrosis", "relation": "adverse effect", "tail": "Kalimate"}, {"head": "colonic mucosal necrosis", "relation": "adverse effect", "tail": "Kayexalate"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
RESULTS : Budesonide use can cause contact dermatitis .
|
adverse effect: contact dermatitis -> Budesonide
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "contact dermatitis", "relation": "adverse effect", "tail": "Budesonide"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
He had been taking trimethoprim - sulfamethoxazole for approximately eight days when he revisited his family physician , complaining of headaches , dizziness , difficulty with speech , weakness , and itching on the trunk of his body and legs , where a maculopapular rash was noted .
|
adverse effect: difficulty with speech -> trimethoprim | adverse effect: dizziness -> sulfamethoxazole | adverse effect: headaches -> trimethoprim | adverse effect: weakness -> sulfamethoxazole | adverse effect: itching on the trunk -> sulfamethoxazole | adverse effect: weakness -> trimethoprim | adverse effect: maculopapular rash -> trimethoprim | adverse effect: headaches -> sulfamethoxazole | adverse effect: maculopapular rash -> sulfamethoxazole | adverse effect: itching on the trunk -> trimethoprim | adverse effect: difficulty with speech -> sulfamethoxazole | adverse effect: dizziness -> trimethoprim
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "difficulty with speech", "relation": "adverse effect", "tail": "sulfamethoxazole"}, {"head": "difficulty with speech", "relation": "adverse effect", "tail": "trimethoprim"}, {"head": "dizziness", "relation": "adverse effect", "tail": "sulfamethoxazole"}, {"head": "dizziness", "relation": "adverse effect", "tail": "trimethoprim"}, {"head": "headaches", "relation": "adverse effect", "tail": "sulfamethoxazole"}, {"head": "headaches", "relation": "adverse effect", "tail": "trimethoprim"}, {"head": "itching on the trunk", "relation": "adverse effect", "tail": "sulfamethoxazole"}, {"head": "itching on the trunk", "relation": "adverse effect", "tail": "trimethoprim"}, {"head": "maculopapular rash", "relation": "adverse effect", "tail": "sulfamethoxazole"}, {"head": "maculopapular rash", "relation": "adverse effect", "tail": "trimethoprim"}, {"head": "weakness", "relation": "adverse effect", "tail": "sulfamethoxazole"}, {"head": "weakness", "relation": "adverse effect", "tail": "trimethoprim"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Arrhythmias and cardiac arrest have been reported during amphotericin B administration but no effective technique has been described to prevent them .
|
adverse effect: cardiac arrest -> amphotericin B | adverse effect: Arrhythmias -> amphotericin B
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "Arrhythmias", "relation": "adverse effect", "tail": "amphotericin B"}, {"head": "cardiac arrest", "relation": "adverse effect", "tail": "amphotericin B"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Fixed drug eruption to rofecoxib .
|
adverse effect: Fixed drug eruption -> rofecoxib
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "Fixed drug eruption", "relation": "adverse effect", "tail": "rofecoxib"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Colonic mucosal necrosis following administration of calcium polystryrene sulfonate ( Kalimate ) in a uremic patient .
|
adverse effect: Colonic mucosal necrosis -> calcium polystryrene sulfonate | adverse effect: Colonic mucosal necrosis -> Kalimate
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "Colonic mucosal necrosis", "relation": "adverse effect", "tail": "calcium polystryrene sulfonate"}, {"head": "Colonic mucosal necrosis", "relation": "adverse effect", "tail": "Kalimate"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Hypersensitivity to zonisamide was confirmed by the skin patch test .
|
adverse effect: Hypersensitivity -> zonisamide
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "Hypersensitivity", "relation": "adverse effect", "tail": "zonisamide"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
Acute respiratory distress syndrome after rituximab infusion .
|
adverse effect: Acute respiratory distress syndrome -> rituximab
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "Acute respiratory distress syndrome", "relation": "adverse effect", "tail": "rituximab"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
CONCLUSION : Healthcare professionals should be aware of the possible development of serotonin syndrome as a complication of initiation of fentanyl and other phenylpiperidine opioids in patients treated with SSRIs .
|
adverse effect: serotonin syndrome -> fentanyl
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "serotonin syndrome", "relation": "adverse effect", "tail": "fentanyl"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
RE
|
ADE_corpus_sample_15000
|
train
|
Extract relationships between entities in the text. Format each relationship as 'relation_type: head_entity -> tail_entity' separated by '|'. If no valid relationships exist, return 'None'.
|
We discuss the association between carbamazepine and hyponatremia and the causes of hyponatremia after cardiopulmonary bypass .
|
adverse effect: hyponatremia -> carbamazepine
|
adverse effect, country capital, members, used for, developer, company industry
|
NYT11: country capital; SciERC_sample_10000: used for; kbp37: members; New-York-Times-RE: company industry; fewrel_4: developer
|
{"relations": [{"head": "hyponatremia", "relation": "adverse effect", "tail": "carbamazepine"}]}
|
You are an expert in Relation Extraction.
Your task is to extract relationships between entities in the given text.
Follow these guidelines:
1. Only extract relations of the types specified in the schema.
2. Format your answer as 'relation_type: head_entity -> tail_entity' separated by '|'.
3. If no relations are found, return 'None'.
4. Be precise - identify the exact direction of each relation.
|
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