Instruction:
Text: With the increasing use of Cyclosporine A in transplant patients , the incidence of herpes esophagitis may increase .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "herpes esophagitis", "tail": "Cyclosporine A"}]


Instruction:
Text: Sudden cardiac death due to hypersensitivity myocarditis during clozapine treatment .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "hypersensitivity myocarditis", "tail": "clozapine"}, {"relation": "adverse effect", "head": "Sudden cardiac death", "tail": "clozapine"}]


Instruction:
Text: We present a fatal case of subacute methanol toxicity with associated diffuse brain involvement , including bilateral putaminal necrosis and cerebral edema with ventricular compression .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "bilateral putaminal necrosis", "tail": "methanol"}, {"relation": "adverse effect", "head": "cerebral edema", "tail": "methanol"}, {"relation": "adverse effect", "head": "diffuse brain involvement", "tail": "methanol"}, {"relation": "adverse effect", "head": "fatal", "tail": "methanol"}, {"relation": "adverse effect", "head": "methanol toxicity", "tail": "methanol"}, {"relation": "adverse effect", "head": "ventricular compression", "tail": "methanol"}]


Instruction:
Text: Livedo reticularis associated with interferon alpha therapy in two melanoma patients .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "Livedo reticularis", "tail": "interferon alpha"}]


Instruction:
Text: Lethargy in a newborn : lithium toxicity or lab error ?
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "Lethargy", "tail": "lithium"}, {"relation": "adverse effect", "head": "lithium toxicity", "tail": "lithium"}]


Instruction:
Text: Fixed drug eruption of the scrotum due to methylphenidate .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "Fixed drug eruption of the scrotum", "tail": "methylphenidate"}]


Instruction:
Text: Neurological side effects in two patients receiving gold injections for rheumatoid arthritis .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "Neurological side effects", "tail": "gold"}]


Instruction:
Text: Withdrawal of Depakote resulted in resolution of the effusion .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "effusion", "tail": "Depakote"}]


Instruction:
Text: A case of colchicine - induced rhabdomyolysis is reported .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "rhabdomyolysis", "tail": "colchicine"}]


Instruction:
Text: In patients with a known access to INH , seizures should be considered to be caused by INH toxicity unless proved otherwise .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "seizures", "tail": "INH"}]


Instruction:
Text: METHODS : A 76-year - old woman with primary open - angle glaucoma and no history of ocular surgery developed a choroidal detachment 12 hours after initiation of therapy with dorzolamide eye drops .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "choroidal detachment", "tail": "dorzolamide"}]


Instruction:
Text: Fulminant hepatic failure developed in a 24-year - old black woman who had been treated with propylthiouracil and propranolol for hyperthyroidism .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "Fulminant hepatic failure", "tail": "propranolol"}, {"relation": "adverse effect", "head": "Fulminant hepatic failure", "tail": "propylthiouracil"}]


Instruction:
Text: The induction of hypoglycaemia with PAS in this patient suggests a potential role for PAS in the treatment of diabetes mellitus .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "hypoglycaemia", "tail": "PAS"}]


Instruction:
Text: The probability of developing acute leukemia in this study was not significantly correlated to the total cumulative dosage of Treosulfan .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "acute leukemia", "tail": "Treosulfan"}]


Instruction:
Text: We report here a case of cardiovascular and neurological depression induced by oxymetalzoline in a toddler .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "neurological depression", "tail": "oxymetalzoline"}]


Instruction:
Text: Here , we report a case of angio - oedema associated with VRC therapy .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "angio - oedema", "tail": "VRC"}]


Instruction:
Text: The syndrome of irreversible lithium - effectuated neurotoxicity ( SILENT ) .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "neurotoxicity", "tail": "lithium"}]


Instruction:
Text: This article presents a case of an atypical localized cutaneous eruption with an unusual course and protracted resolution time associated with sorafenib therapy .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "atypical localized cutaneous eruption", "tail": "sorafenib"}]


Instruction:
Text: This report presents a potential case of risperidone - induced tardive dyskinesia .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "tardive dyskinesia", "tail": "risperidone"}]


Instruction:
Text: Myoclonic spasms following intrathecal morphine .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "Myoclonic spasms", "tail": "morphine"}]


Instruction:
Text: An 8-year - old girl , who had been vaccinated with BCG without subsequent regional reactions , developed osteomyelitis in the left calcaneus 7 months later .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "osteomyelitis in the left calcaneus", "tail": "BCG"}]


Instruction:
Text: After having received gemcitabine on day 1 of the second course , the patient developed dry cough , subfebrile temperatures and dyspnea within 48 h .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "dry cough", "tail": "gemcitabine"}, {"relation": "adverse effect", "head": "dyspnea", "tail": "gemcitabine"}, {"relation": "adverse effect", "head": "subfebrile temperatures", "tail": "gemcitabine"}]


Instruction:
Text: Serotonin syndrome induced by transitioning from phenelzine to venlafaxine : four patient reports .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "Serotonin syndrome", "tail": "phenelzine"}, {"relation": "adverse effect", "head": "Serotonin syndrome", "tail": "venlafaxine"}]


Instruction:
Text: Both colchicine and statin therapy may be associated with myopathy , which usually occurs after several months of therapy .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "myopathy", "tail": "colchicine"}, {"relation": "adverse effect", "head": "myopathy", "tail": "statin"}]


Instruction:
Text: Amiodarone hydrochloride , a new antiarrhythmic agent , has been associated with pulmonary toxicity characterized by cough , dyspnea and diffuse pulmonary infiltrates .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "cough", "tail": "Amiodarone hydrochloride"}, {"relation": "adverse effect", "head": "diffuse pulmonary infiltrates", "tail": "Amiodarone hydrochloride"}, {"relation": "adverse effect", "head": "dyspnea", "tail": "Amiodarone hydrochloride"}, {"relation": "adverse effect", "head": "pulmonary toxicity", "tail": "Amiodarone hydrochloride"}]


Instruction:
Text: Pregabalin - induced cortical negative myoclonus in a patient with neuropathic pain .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "cortical negative myoclonus", "tail": "Pregabalin"}]


Instruction:
Text: We report a male patient with advanced AIDS who developed hypercalcemia 2 weeks after institution of rhGH therapy .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "hypercalcemia", "tail": "rhGH"}]


Instruction:
Text: Massive CBZ OD may produce a reversible encephalopathy that includes cortical hyperexcitability , a profound burst - suppression EEG pattern , and cranial nerve areflexia .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "cortical hyperexcitability", "tail": "CBZ"}, {"relation": "adverse effect", "head": "cranial nerve areflexia", "tail": "CBZ"}, {"relation": "adverse effect", "head": "profound burst - suppression EEG pattern", "tail": "CBZ"}, {"relation": "adverse effect", "head": "reversible encephalopathy", "tail": "CBZ"}]


Instruction:
Text: A 35-year - old man with seropositive rheumatoid arthritis developed selective IgA deficiency during treatment with fenclofenac .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "IgA deficiency", "tail": "fenclofenac"}]


Instruction:
Text: Minor electrocardiographical changes were noted in five out of six patients who were not receiving a cardiac glycoside and four out of six who were receiving ouabain , and none of the 16 who were receiving digoxin .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "Minor electrocardiographical changes", "tail": "ouabain"}]


Instruction:
Text: Severe hepatotoxicity from phenobarbital occurred in an infant boy who had a complicated illness with chronic bilateral subdural hematomas and sepsis .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "Severe hepatotoxicity", "tail": "phenobarbital"}]


Instruction:
Text: We observed 2 cases of hepatotoxicity after a high - dose methylprednisolone treatment of a demyelinating disease and evaluated the potential relationship in the light of available evidence .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "hepatotoxicity", "tail": "methylprednisolone"}]


Instruction:
Text: In both cases , high fever , skin rash , liver dysfunction and atypical lymphocytosis developed 3 weeks after initiating treatment with SASP .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "atypical lymphocytosis", "tail": "SASP"}, {"relation": "adverse effect", "head": "high fever", "tail": "SASP"}, {"relation": "adverse effect", "head": "liver dysfunction", "tail": "SASP"}, {"relation": "adverse effect", "head": "skin rash", "tail": "SASP"}]


Instruction:
Text: A patient with severe cholestatic jaundice induced by captopril is presented .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "severe cholestatic jaundice", "tail": "captopril"}]


Instruction:
Text: We present a case of a sustained monomorphic ventricular tachycardia following adenosine infusion .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "sustained monomorphic ventricular tachycardia", "tail": "adenosine"}]


Instruction:
Text: Acute hemorrhagic gastritis associated with acetazolamide intoxication in a patient with chronic renal failure .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "acetazolamide intoxication", "tail": "acetazolamide"}, {"relation": "adverse effect", "head": "Acute hemorrhagic gastritis", "tail": "acetazolamide"}]


Instruction:
Text: Ramipril - induced cutaneous vasculitis is particularly rare and our case was atypical because the patient had tolerated lisinopril before .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "cutaneous vasculitis", "tail": "Ramipril"}]


Instruction:
Text: Gabapentin toxicity in renal failure : the importance of dose adjustment .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "renal failure", "tail": "Gabapentin"}]


Instruction:
Text: Five patients are described in whom only gentamicin sulfate appeared responsible for acute renal failure .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "acute renal failure", "tail": "gentamicin sulfate"}]


Instruction:
Text: Based on the Naranjo algorithm , the adverse reaction observed was probably related to the hydroxyurea treatment ( score = 6 ) ; however , the hydroxyurea chemotherapy could not be discontinued because of the myeloproliferative disorder .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "myeloproliferative disorder", "tail": "hydroxyurea"}]


Instruction:
Text: Use of the Naranjo probability scale determined the association between cephalosporin use and leukopenia to be probable .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "leukopenia", "tail": "cephalosporin"}]


Instruction:
Text: Fatal interstitial pneumonitis following high - dose intermittent chlorambucil therapy for chronic lymphocyte leukemia .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "Fatal interstitial pneumonitis", "tail": "chlorambucil"}]


Instruction:
Text: Pulmonary fibrosis subsequent to high doses of CCNU for chronic myeloid leukemia .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "Pulmonary fibrosis", "tail": "CCNU"}]


Instruction:
Text: Nitrofurantoin - induced acute liver damage in pregnancy .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "acute liver damage", "tail": "Nitrofurantoin"}]


Instruction:
Text: The use of pamidronate for hypercalcemia secondary to acute vitamin D intoxication .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "vitamin D intoxication", "tail": "vitamin D"}]


Instruction:
Text: We report a case of a women in whom a malignant mixed mesodermal tumor was diagnosed while she was taking raloxifene , which is also a selective estrogen receptor modulator .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "malignant mixed mesodermal tumor", "tail": "raloxifene"}]


Instruction:
Text: CONCLUSION : There are only a few confirmed cases of gemcitabine - associated HUS despite the widespread use of the drug .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "HUS", "tail": "gemcitabine"}]


Instruction:
Text: Onset of male gynaecomastia in a patient treated with sunitinib for metastatic renal cell carcinoma .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "gynaecomastia", "tail": "sunitinib"}]


Instruction:
Text: A case of fluoxetine induced seizures , in a person with Down syndrome , is described .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "seizures", "tail": "fluoxetine"}]


Instruction:
Text: We report a case of a 60-year - old woman affected by squamous lung carcinoma , who developed paroxysmal supraventricular tachycardia during cisplatin and etoposide combination chemotherapy .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "paroxysmal supraventricular tachycardia", "tail": "cisplatin"}, {"relation": "adverse effect", "head": "paroxysmal supraventricular tachycardia", "tail": "etoposide"}]


Instruction:
Text: Common adverse events ( frequency 10 % ) of lacosamide doses up to 600 mg / day include nonspecific central nervous system effects ( e.g. , dizziness , ataxia , diplopia , and somnolence ) .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "ataxia", "tail": "lacosamide"}, {"relation": "adverse effect", "head": "diplopia", "tail": "lacosamide"}, {"relation": "adverse effect", "head": "dizziness", "tail": "lacosamide"}, {"relation": "adverse effect", "head": "somnolence", "tail": "lacosamide"}]


Instruction:
Text: We report a case of a patient with rheumatoid arthritis treated with low - dose methotrexate ( 15 mg / week ) who developed infection with both M. tuberculosis and M. chelonae after the revision of a prosthetic hip .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "infection with both M. tuberculosis and M. chelonae", "tail": "methotrexate"}]


Instruction:
Text: Two patients had ampicillin - associated seizures .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "seizures", "tail": "ampicillin"}]


Instruction:
Text: Valproate embryopathy in three sets of siblings : further proof of hereditary susceptibility .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "Valproate embryopathy", "tail": "Valproate"}]


Instruction:
Text: A literature review revealed 83 other reported cases of rifampicin - induced renal insufficiency .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "renal insufficiency", "tail": "rifampicin"}]


Instruction:
Text: A 60-year - old white man with chronic bronchitis was noted to develop acute respiratory failure and metabolic acidosis four days after being started on methazolamide ( Neptazane ) for an ophthalmologic problem .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "acute respiratory failure", "tail": "methazolamide"}, {"relation": "adverse effect", "head": "acute respiratory failure", "tail": "Neptazane"}, {"relation": "adverse effect", "head": "metabolic acidosis", "tail": "methazolamide"}, {"relation": "adverse effect", "head": "metabolic acidosis", "tail": "Neptazane"}]


Instruction:
Text: On the fifth day of tocolysis with magnesium sulfate , nifedipine , terbutaline and betamethasone , edema developed in both labia .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "edema", "tail": "betamethasone"}, {"relation": "adverse effect", "head": "edema", "tail": "magnesium sulfate"}, {"relation": "adverse effect", "head": "edema", "tail": "nifedipine"}, {"relation": "adverse effect", "head": "edema", "tail": "terbutaline"}]


Instruction:
Text: Thrombotic microangiopathy with renal failure in two patients undergoing gemcitabine chemotherapy .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "renal failure", "tail": "gemcitabine"}, {"relation": "adverse effect", "head": "Thrombotic microangiopathy", "tail": "gemcitabine"}]


Instruction:
Text: Acute esmolol toxicity may be self - limiting because of its extremely short half - life .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "esmolol toxicity", "tail": "esmolol"}]


Instruction:
Text: We describe the exceptional development of AML and lung cancer in a patient with previously diagnosed CLL in minimal residual disease status after fludarabine treatment followed by autologous peripheral blood stem - cell transplantation .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "AML", "tail": "fludarabine"}, {"relation": "adverse effect", "head": "lung cancer", "tail": "fludarabine"}]


Instruction:
Text: Continuous EEG monitoring is helpful in managing seizures that occur as a complication of CBZ OD , after the course of recovery or worsening , and in providing assistance with prognosis .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "seizures", "tail": "CBZ"}]


Instruction:
Text: DATA SYNTHESIS : Genetic deficiencies in DPD , the rate - limiting enzyme responsible for 5-FU catabolism , may occur in 3 % or more of patients with cancer putting them at increased risk for unusually severe adverse reactions ( e.g. , diarrhea , stomatitis , mucositis , myelosuppression , neurotoxicity ) to standard doses of 5-FU .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "diarrhea", "tail": "5-FU"}, {"relation": "adverse effect", "head": "diarrhea", "tail": "5-FU"}, {"relation": "adverse effect", "head": "mucositis", "tail": "5-FU"}, {"relation": "adverse effect", "head": "mucositis", "tail": "5-FU"}, {"relation": "adverse effect", "head": "myelosuppression", "tail": "5-FU"}, {"relation": "adverse effect", "head": "myelosuppression", "tail": "5-FU"}, {"relation": "adverse effect", "head": "neurotoxicity", "tail": "5-FU"}, {"relation": "adverse effect", "head": "neurotoxicity", "tail": "5-FU"}, {"relation": "adverse effect", "head": "stomatitis", "tail": "5-FU"}, {"relation": "adverse effect", "head": "stomatitis", "tail": "5-FU"}]


Instruction:
Text: Clofibrate - induced myopathy in patients with diabetes insipidus .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "myopathy", "tail": "Clofibrate"}]


Instruction:
Text: This is the first case of HBV reactivation occurring during the year following rituximab monotherapy in the absence of any other immunosuppressive factor .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "HBV reactivation", "tail": "rituximab"}]


Instruction:
Text: Theophylline intoxication mimicking diabetic ketoacidosis in a child .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "Theophylline intoxication", "tail": "Theophylline"}]


Instruction:
Text: Four patients who manifested symptoms of the antiepileptic drug ( AED ) hypersensitivity syndrome during therapy with carbamazepine are reported .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "hypersensitivity syndrome", "tail": "carbamazepine"}]


Instruction:
Text: A 53-year - old man developed NMS without rigidity while taking olanzapine .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "NMS", "tail": "olanzapine"}]


Instruction:
Text: The authors describe pericardial hemorrhage , which is related to the use of low - dose acetylsalicylic acid in a patient with essential thrombocythemia .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "pericardial hemorrhage", "tail": "acetylsalicylic acid"}]


Instruction:
Text: Three of 50 patients treated with isotretinoin ( 1 mg / kg / day ) for cystic acne complained of poor night vision and/or excessive glare sensitivity .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "excessive glare sensitivity", "tail": "isotretinoin"}, {"relation": "adverse effect", "head": "poor night vision", "tail": "isotretinoin"}]


Instruction:
Text: When measured , the serum lithium level had increased 4-fold during acyclovir therapy .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "serum lithium level had increased", "tail": "acyclovir"}, {"relation": "adverse effect", "head": "serum lithium level had increased", "tail": "lithium"}]


Instruction:
Text: Alopecia , nausea , and vomiting were attributed to the cyclophosphamide component of the therapy .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "Alopecia", "tail": "cyclophosphamide"}, {"relation": "adverse effect", "head": "nausea", "tail": "cyclophosphamide"}, {"relation": "adverse effect", "head": "vomiting", "tail": "cyclophosphamide"}]


Instruction:
Text: We report here a case of TEN after administration of ciprofloxacin .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "TEN", "tail": "ciprofloxacin"}]


Instruction:
Text: We believe this is the first report of intrathecal diamorphine causing anaphylaxis .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "anaphylaxis", "tail": "diamorphine"}]


Instruction:
Text: A 35-year - old nephrotic man developed acute renal failure with serum creatinine to 1543 micromol / l after a month of therapy with enalapril .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "acute renal failure", "tail": "enalapril"}]


Instruction:
Text: Fluoxetine - related death in a child with cytochrome P-450 2D6 genetic deficiency .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "death", "tail": "Fluoxetine"}]


Instruction:
Text: Lithium neurotoxicity should be considered in Creutzfeldt - Jakob disease differential diagnosis , serial electroencephalograms being the most valuable .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['adverse effect']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "adverse effect", "head": "Creutzfeldt - Jakob disease", "tail": "Lithium"}, {"relation": "adverse effect", "head": "Lithium neurotoxicity", "tail": "Lithium"}]


Instruction:
Text:  Gov. Tim Pawlenty of Minnesota ordered the state health department this month to monitor day-to-day operations at the Minneapolis Veterans Home after state inspectors found that three men had died there in the previous month because of neglect or medical errors .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place lived", "head": "Tim Pawlenty", "tail": "Minnesota"}]


Instruction:
Text:  The aid group Doctors Without Borders said that since Saturday , more than 275 wounded people had been admitted and treated at Donka Hospital in the capital of Guinea , Conakry .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "country capital", "head": "Guinea", "tail": "Conakry"}]


Instruction:
Text:  The American people can see what is happening here , '' said Senator Ben Nelson , Democrat of Nebraska . ''
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place lived", "head": "Ben Nelson", "tail": "Nebraska"}]


Instruction:
Text:  For millions , it was a tough day of coping of watching floodwater pour into homes on the Raritan River in New Jersey from New Brunswick to Bound Brook and in the Westchester suburbs of Mamaroneck and New Rochelle , of sleeping in a shelter or an airport , of towing a car and watching a refrigerator float by , of getting to work despite flooded roads and erratic trains , of waiting for power or a water pump or just something to hope for . ''
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Westchester", "tail": "Mamaroneck"}]


Instruction:
Text:  The United States maintains diplomatic relations with Syria , but State Department officials say no deeper engagement is warranted until the government in Damascus halts support for radical organizations , including Hezbollah in Lebanon and Hamas in the Palestinian territories . ''
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "administrative division of country", "head": "Damascus", "tail": "Syria"}]


Instruction:
Text:  Allen J. Ross , President $ 6 $ GOLDBERG Eli Malcom , Age 93 , at home in Boca Raton , Florida , January 20 , 2007 .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Florida", "tail": "Boca Raton"}]


Instruction:
Text:  It 's irrational , '' said Peter Bragdon , the general counsel at Columbia Sportswear , one of the companies suing the government .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "company", "head": "Peter Bragdon", "tail": "Columbia Sportswear"}]


Instruction:
Text:  In essence this can start to rewrite the history of photography , '' said Grant Romer , director of the advanced residency program in photograph conservation at the George Eastman House in Rochester . ''
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Rochester", "tail": "George Eastman House"}]


Instruction:
Text:  The Democratic presidential contenders are appearing Wednesday in Carson City , Nev. , for a forum sponsored by the American Federation of State , County and Municipal Employees ; George Stephanopoulos of ABC News will be asking them questions .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "company", "head": "George Stephanopoulos", "tail": "ABC News"}]


Instruction:
Text:  In many ways , the donor conference here on Thursday will serve as a snapshot of the larger proxy war between the United States joined by its Sunni Arab allies and Europe and Iran and Syria .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "administrative division of country", "head": "Syria", "tail": "Iran"}]


Instruction:
Text:  Carbon is like a chair , and nitrogen is like a three-legged stool , '' said Dr. Geraldine Richmond , professor of chemistry at the University of Oregon in Eugene .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Eugene", "tail": "University of Oregon"}]


Instruction:
Text:  INDIAN AIRLINE EXPANDING Jet Airways , the largest domestic carrier in India , says it will start service to the United States in late August with daily flights to Kennedy International Airport from Delhi , via Brussels .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "administrative division of country", "head": "Delhi", "tail": "India"}]


Instruction:
Text:  A writer 's yearlong journey in search of self takes her to Italy , India and Indonesia .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "administrative division of country", "head": "Italy", "tail": "India"}]


Instruction:
Text:  But Williams said she did take enormous pleasure in her 10-day visit to Senegal and Ghana in West Africa in November .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "West Africa", "tail": "Senegal"}]


Instruction:
Text:  Isabel Hoving of the Netherlands , Rita Williams-Garcia of the United States and Markus Zusak of Australia will participate .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "nationality", "head": "Markus Zusak", "tail": "Australia"}]


Instruction:
Text:  It 's more than I could imagine , '' said Dr. Gurinder Singh , 55 , an ophthalmologist visiting from Kansas City , Kan. '' This Irish culture is marvelous , and it is still being represented after so many years of immigrants being here , '' said Dr. Singh , who is originally from the state of Punjab , in India .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "India", "tail": "Punjab"}]


Instruction:
Text:  Many Y.B.A. 's , as young British artists were known in the 1990s , are now household names in France , while London 's share of the global art market last year was seven times larger than that of Paris .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "administrative division of country", "head": "Paris", "tail": "France"}]


Instruction:
Text:  Panama City Beach , too , has a glut of condos , but the area was one of only two in Florida where sales rose in March , compared with a year earlier .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Florida", "tail": "Panama City Beach"}]


Instruction:
Text:  As a result of the war , Hezbollah was pushed off the border by Israel and , in its place , the U.N. inserted a new peacekeeping force of some 10,000 troops , including a big European contingent , led by France and Italy .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "administrative division of country", "head": "Italy", "tail": "Israel"}]


Instruction:
Text:  Woody Harrelson 's Father Dies in Prison Woody Harrelson 's father , Charles Harrelson , 68 , has died in the high-security federal prison in Florence , Colo. , where he was serving two life sentences for the murder of a federal judge , The Associated Press reported yesterday .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "children", "head": "Charles Harrelson", "tail": "Woody Harrelson"}]


Instruction:
Text:  But Mr. Leverett said the threat to American troops from Sunni insurgents , who draw on Syria and Saudi Arabia for money and other logistical support , was '' orders of magnitude '' greater than that from Shiites , and he contended that the Bush administration 's public emphasis on the E.F.P. 's was part of a larger administration strategy to blame Iran '' for the failure of the American project in Iraq . ''
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "administrative division of country", "head": "Iran", "tail": "Syria"}]


Instruction:
Text:  Both are now building striking new satellites the Louvre in Lens northwest of Paris and the Pompidou in Metz in eastern France to display parts of their collection , much of which is permanently in storage .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "administrative division of country", "head": "Paris", "tail": "France"}]


Instruction:
Text:  In northern Gaza on Monday , Israeli Air Force planes fired on a car that army officials said was laden with explosives and on its way to launch rockets into Israel .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "administrative division of country", "head": "Gaza", "tail": "Israel"}]


Instruction:
Text:  ET CETERA The low-cost airline FlyGlobespan has started thrice-weekly service between Kennedy International Airport and West Airport Knock in County Mayo , in Ireland .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Ireland", "tail": "County Mayo"}]


Instruction:
Text:  It was midnight here in Hanoi , or already 2 a.m. back in Seoul , South Korea .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "South Korea", "tail": "Seoul"}]


Instruction:
Text:  Sony Corporation wishes to express its deepest and most heartfelt sympathy to his family , along with its utmost respect and gratitude for the instrumental role he played in assisting his middle school classmate and Sony founder , Akio Morita , in establishing Sony 's presence in the United States in the early 1960s .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "company founders", "head": "Sony", "tail": "Akio Morita"}]


Instruction:
Text:  8 P.M. Fox AMERICAN IDOL Southern discomfort as Week 3 auditions begin in Birmingham , Ala. 9 P.M. (13) THE CELL NEXT DOOR Last summer , 18 young men in Toronto and two in Atlanta most of whom had grown up in moderate , middle-class homes in Canada and the United States were arrested on terrorism charges after being accused of plotting to blow up buildings and behead members of the Canadian parliament and attending a terrortraining camp in Ontario .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "administrative division of country", "head": "Ontario", "tail": "Canada"}]


Instruction:
Text:  In Germany , unlike Italy , defendants can not be tried in absentia .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "administrative division of country", "head": "Italy", "tail": "Germany"}]


Instruction:
Text:  This is the first of many products that are going to come out of the AT&T BellSouth merger , '' said Ralph de la Vega , group president of regional wireline operations for AT&T , of San Antonio .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "company", "head": "Ralph de la Vega", "tail": "AT&T"}]


Instruction:
Text:  Even the emergency agency 's newest maps , scheduled to go into use this fall which show slightly enlarged flood zones on the south shores of Staten Island and Queens , for example ; along the Hudson River in Lower Manhattan ; and in Hunts Point in the Bronx are still based only on historical data .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Lower Manhattan", "tail": "Hudson River"}]


Instruction:
Text:  Melissa Perlman , a mother of an 11-month-old and founder of Amansala Eco Chic Resorts www.amansala.com in Tulum , Mexico , knows firsthand that her yoga-inspired babymoons give expectant parents much-needed time to bond and regroup . ''
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Mexico", "tail": "Tulum"}]


Instruction:
Text:  And they would never understand why , for Bill Elliott , there was no joy in Dawsonville .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place lived", "head": "Bill Elliott", "tail": "Dawsonville"}]


Instruction:
Text:  New Jersey 911 centers routinely receive calls from across the Hudson in Westchester or across the Delaware River at the Pennsylvania border .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Westchester", "tail": "Hudson"}]


Instruction:
Text:  Born in Calgary , Canada and raised in Portland , Oregon , Jean died peacefully at home June 6 , 2007 .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Canada", "tail": "Calgary"}]


Instruction:
Text:  Ms. Kahf came to this country in 1971 from Damascus , Syria , before her fourth birthday , and like her , many immigrant Muslim children find themselves caught between hostile worlds at school and parents who are basically clueless .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "administrative division of country", "head": "Damascus", "tail": "Syria"}]


Instruction:
Text:  The National Ignition Facility at Lawrence Livermore National Laboratory in California is to start testing that idea around 2010 .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "California", "tail": "Lawrence Livermore National Laboratory"}]


Instruction:
Text:  The administration is now put in a position of playing defense , as we are finally seeing the international consequences of the rather dramatic internal transformation inside Russia with the erosion of democracy , a new ruling class , a massive transfer of property rights from so-called oligarchs to , basically , friends of Putin , most of whom are from the old K.G.B. '' Senior administration officials said the new effort to reach out to Russia already had been put into action , and included Secretary of State Condoleezza Rice 's consultation with her Russian counterpart , Foreign Minister Sergey V. Lavrov , during meetings in Berlin on Feb. 21 and 22 about whether Russia had the appetite to pursue a second Security Council resolution for sanctions against Iran .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "administrative division of country", "head": "Russia", "tail": "Iran"}]


Instruction:
Text:  Senators Joseph R. Biden Jr. , Democrat of Delaware , and Chuck Hagel , Republican of Nebraska , said they would back Mr. Warner 's alternative , which declares that '' the Senate disagrees with the ` plan ' to augment our forces by 21,500 , '' calls on the president to consider other alternatives and urges him to limit the American role in countering sectarian violence . ''
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place lived", "head": "Chuck Hagel", "tail": "Nebraska"}]


Instruction:
Text:  Speakers included David Bonderman of the Texas Pacific Group , which helped to lead Monday 's $ 45 billion TXU takeover ; Todd Fisher of Kohlberg Kravis Roberts & Company , the other lead investor in the TXU deal ; and Stephen A. Schwarzman of the Blackstone Group , considered one of the most likely candidates to try to crash the TXU sale .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "company", "head": "Stephen A. Schwarzman", "tail": "Blackstone Group"}]


Instruction:
Text:  All came from Nuremberg , Germany , a center of brass production since the Middle Ages .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Germany", "tail": "Nuremberg"}]


Instruction:
Text:  Stephen A. Schwarzman , the co-founder of the Blackstone Group , which is in the process of going public , made $ 400 million last year .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "company founders", "head": "Blackstone Group", "tail": "Stephen A. Schwarzman"}]


Instruction:
Text:  Senator Chuck Hagel of Nebraska , who has been the loudest Republican critic of the president 's Iraq policy , also signed the letter after his vote contributed to a delay of the Iraq debate . ''
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place lived", "head": "Chuck Hagel", "tail": "Nebraska"}]


Instruction:
Text:  There 's chatter about Mr. Zeitlin 's sweeping sideways motion dubbed '' the Zeitlin sidewinder ; '' Alex Porush 's wicked topspin loops ; the way Bill Vogel , a wine distributor from New Rochelle , and Will Shortz , the crossword puzzle editor of The New York Times , employ the rather daring Seemiller style of play , which uses only one side of the paddle .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "company", "head": "Will Shortz", "tail": "The New York Times"}]


Instruction:
Text:  The companies have headquarters just 15 miles apart Hologic in Bedford , Mass. , and Cytyc in Marlborough , Mass. .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Marlborough", "tail": "Cytyc"}]


Instruction:
Text:  Ask people in Waterloo , Iowa .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Iowa", "tail": "Waterloo"}]


Instruction:
Text:  Mrs. Somary is the managing director of the Mendelssohn Project , a nonprofit organization founded in Manhattan and Stuttgart , Germany , by the bridegroom , its music director .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Germany", "tail": "Stuttgart"}]


Instruction:
Text:  '' Dreaming Lhasa , '' a dramatic feature film from the documentarians Ritu Sarin and Tenzing Sonam , is about a Tibetan-American documentary filmmaker named Karma Tenzin Chokyi Gyatso making a movie about the plight of Tibetans who fled persecution by the Chinese and settled in Dharamsala , the Dalai Lama 's exile headquarters in northern India .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "India", "tail": "Dharamsala"}]


Instruction:
Text:  The trucks entered Gaza from Israel at the Kerem Shalom crossing , near the spot where the borders of Gaza , Israel and Egypt converge .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "administrative division of country", "head": "Gaza", "tail": "Israel"}]


Instruction:
Text:  Its interest aroused , the World Jewish Congress asked Professor Herzstein , the scholar of Nazi history , to comb the National Archives in Washington for evidence of Mr. Waldheim 's possible involvement in war crimes .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Washington", "tail": "National Archives"}]


Instruction:
Text:  Miikka Ryokas , whose user name is Kizor and in an e-mail message said that he was a 22-year-old computer science student from Turku , Finland , wrote : '' As the popular joke goes , ` The problem with Wikipedia is that it only works in practice .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Finland", "tail": "Turku"}]


Instruction:
Text:  Her mother teaches online courses in Sanskrit for Potti Sreeramulu Telugu University in Hyderabad , India .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Hyderabad", "tail": "Potti Sreeramulu Telugu University"}]


Instruction:
Text:  During the 40-year occupation of the West Bank and Gaza , Al Qaeda was never involved in terrorist attacks against Israel .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "administrative division of country", "head": "Gaza", "tail": "Israel"}]


Instruction:
Text:  He is survived by his wife , Margaret Terry Red Evans , of Bainbridge Island , Washington ; a brother , Elwyn Evans , and sister , Katharine Reese May , both of Wilmington , Delaware ; a daughter , Shelley Evans , of Cambridge , Massachusetts ; a son , Lloyd Tilghman Boyd Evans of Rosemont , New Jersey , and five grandchildren .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Washington", "tail": "Bainbridge Island"}]


Instruction:
Text:  The game is big at Lake Forest , a liberal arts college of 1,400 students on Lake Michigan in Chicago 's northern suburbs .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "administrative division of country", "head": "Lake Michigan", "tail": "Chicago"}]


Instruction:
Text:  Mr. Taylor begins , therefore , with the awkward , hastily improvised partition of Germany and Berlin after the war , and the irreconcilable differences that would make Berlin a trigger for potential East-West conflict , and an insoluble problem for both adversaries , especially as German leaders , East and West , learned how to manipulate their masters .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "administrative division of country", "head": "Berlin", "tail": "Germany"}]


Instruction:
Text:  Niall Gunn , the owner of a pub in Rockville Centre , N.Y. , said that St. Patrick 's Day was always a laid-back affair in his hometown in County Monaghan , Ireland , but times have changed . ''
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Ireland", "tail": "County Monaghan"}]


Instruction:
Text:  In remote rural corners of India , particularly in conflict zones like Chhattisgarh , police ranks are woefully understaffed , and isolated police posts are among the rebels ' favorite targets .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "India", "tail": "Chhattisgarh"}]


Instruction:
Text:  Mr. Abbas is trying to persuade Palestinian factions to halt the rocket fire from Gaza and work toward a new cease-fire with Israel that would extend to the West Bank .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "administrative division of country", "head": "Israel", "tail": "West Bank"}]


Instruction:
Text:  So when officials in the Town of Clarkstown , which includes New City and several other hamlets , started thinking about the drab wall across from the Town Hall on a heavily trafficked road , they thought , '' Why not let them do the wall as well ? ''
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Clarkstown", "tail": "New City"}]


Instruction:
Text:  WEEK 'S NOTABLES CLUBS In Europe , four teams clinched first place last weekend : Inter Milan in Italy 's Serie A ; Olympique Lyon in France 's Ligue 1 ; Glasgow Celtic in the Scottish Premier League ; and Olympiakos Piraeus in Greece .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "administrative division of country", "head": "Italy", "tail": "France"}]


Instruction:
Text:  It 's beautiful , '' said Marlene Peacock , a tourist from Tobermory , Ontario , gazing up at the forested hills . ''
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Ontario", "tail": "Tobermory"}]


Instruction:
Text:  Cities like New York grow in their unbuilding : demolition tends to precede development , most urgently and particularly in Lower Manhattan , where New York City began .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "neighborhood of", "head": "Lower Manhattan", "tail": "New York City"}]


Instruction:
Text:  Representative Tom Latham , Republican of Iowa , also voted for the interest cuts . ''
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place lived", "head": "Tom Latham", "tail": "Iowa"}]


Instruction:
Text:  Mr. Putin will continue his Middle Eastern trip Tuesday by traveling to Amman , Jordan , where he will meet with King Abdullah II to discuss economic and political cooperation , and meet with the president of the Palestinian Authority , Mahmoud Abbas .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Jordan", "tail": "Amman"}]


Instruction:
Text:  Israel , citing security reasons , has largely isolated Gaza , strictly controlling the traffic of people and goods among Israel , Gaza and the West Bank a policy which Palestinians say has led to further impoverishment .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "administrative division of country", "head": "Israel", "tail": "West Bank"}]


Instruction:
Text:  The issue of security was brought to the forefront of the presidential campaign last week after an eight-hour standoff between protesters and the police at the M9tro station at the Gare du Nord in Paris .\r\n
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Paris", "tail": "Gare du Nord"}]


Instruction:
Text:  He went back to Mexico , where he published two books of verse , and then began a long period of displacement and travel and drug-taking and odd jobs in France and Spain .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "administrative division of country", "head": "France", "tail": "Spain"}]


Instruction:
Text:  Dear father of Leslie M. Cross of Long Island , NY , Barbara Cross Roy Call of Cleveland Heights , OH , Laurie Cross Gerald Knorr of Seattle , Washington , Steven Frederick Cross of Olympia , Washington , and David Brian Cross Lucia Bowen Cross of Wilton , CT .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Washington", "tail": "Olympia"}]


Instruction:
Text:  There are plenty of critics , '' Brad Henry , Oklahoma 's Democratic governor , told me , shortly before calling for universal preschool for 3-year-olds in his State of the State address on Monday . ''
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place lived", "head": "Brad Henry", "tail": "Oklahoma"}]


Instruction:
Text:  Bono said that President Jacques Chirac of France had spoken eloquently of the need to support Africa , though he added that France had not yet come through with the resources .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "nationality", "head": "Jacques Chirac", "tail": "France"}]