Instruction:
Text: Certainly the Berlin Philharmonic that appeared at Carnegie Hall on Wednesday night , in the first of four programs through tomorrow night , looks a lot different from the ensemble of even 10 years ago , when the great Claudio Abbado was 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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place lived", "head": "Claudio Abbado", "tail": "Berlin"}]


Instruction:
Text: Gov. Haley Barbour of Mississippi acknowledges that Clyde Kennard suffered a grievous wrong at the hands of state officials more than 45 years ago .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place lived", "head": "Haley Barbour", "tail": "Mississippi"}, {"relation": "place lived", "head": "Clyde Kennard", "tail": "Mississippi"}]


Instruction:
Text: A sports article on Aug. 11 about an underground effort by Christopher Vandaele , the owner of a triathlon outfitter in Manhattan , to stage a triathlon in New York City referred incorrectly to a cease-and-desist order demanding that he not use the World Triathlon Corporation 's '' Iron '' brand in promoting the race .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

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


Instruction:
Text: No Tie to Serial Killer Is Found in 7 Deaths Charles Cullen , the nurse who killed patients in New Jersey and Pennsylvania , has so far pleaded guilty to intentionally giving lethal doses of drugs to 24 people during a 16-year career in which he worked at nine hospitals and a nursing home in the two states .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place lived", "head": "Charles Cullen", "tail": "Pennsylvania"}]


Instruction:
Text: Matter of fact , we had one guy just two weeks out of training at Fort Hood , Texas .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Texas", "tail": "Fort Hood"}]


Instruction:
Text: A senior aide to Grand Ayatollah Ali al-Sistani , Iraq 's most influential Shiite cleric , was gunned down on his way to Friday Prayer in Baghdad , and a suicide car bomb exploded near the office of a major Shiite political party , an Interior Ministry official said Friday .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "country of capital", "head": "Iraq", "tail": "Baghdad"}, {"relation": "location contains", "head": "Iraq", "tail": "Baghdad"}]


Instruction:
Text: The Kharkh has been struck repeatedly , most recently two weeks ago , placing great pressure on Baghdad 's mayor , Alaa al-Tamimi , and the American forces to carry out repairs and keep the plant running .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place lived", "head": "Alaa al-Tamimi", "tail": "Baghdad"}]


Instruction:
Text: Also on Friday , American marines said they were investigating the death of an Iraqi civilian that occurred last Saturday during raids of houses near Haditha , a city in the troubled Anbar Province in western 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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Iraq", "tail": "Haditha"}]


Instruction:
Text: The movie begins in New York City , where the aspiring musician Luke Falcon -LRB- played by the musician and actor Steven Strait -RRB- has a brief but heart-stopping encounter with a willowy model named Brier -LRB- Pell James -RRB- .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place lived", "head": "Steven Strait", "tail": "New York City"}]


Instruction:
Text: His father was a New York City firefighter in Brooklyn and later a chiropractor , who practiced in Eastchester , N.Y. WEDDINGS\/CELEBRATIONS
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

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


Instruction:
Text: Jean Rosenbluth , a law professor at the University of Southern California and a former federal prosecutor in Los Angeles , said defense lawyers in similar situations had argued that such dismissals were subterfuges involving jurors who were holding out for acquittal . ''
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Los Angeles", "tail": "University of Southern California"}]


Instruction:
Text: She is a second-year resident in psychiatry at Mount Sinai Hospital in New York .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "New York", "tail": "Mount Sinai Hospital"}]


Instruction:
Text: Rose Valentin , a community associate of the New York City clerk 's office , officiated at Boylan Studios , an event space in New York , in a ceremony led by Anthony V. DeMayo , a judge trial referee of the Connecticut Superior Court in New Haven , Conn. .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

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


Instruction:
Text: Margaret Somerville , professor of law and medicine at McGill University , said the ruling '' is extremely important '' in large part because '' the provinces that want to run some form of a complementary private system would probably be able to do so now . ''
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "person of company", "head": "Margaret Somerville", "tail": "McGill University"}]


Instruction:
Text: The case was brought to the Supreme Court by Jacques Chaoulli , a Montreal family doctor who argued his own case through the courts , and George Zeliotis , a chemical salesman who was forced to wait a year for a hip replacement while he was prohibited from paying privately for surgery .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place lived", "head": "Jacques Chaoulli", "tail": "Montreal"}]


Instruction:
Text: Though officials in Addis Ababa , Ethiopia 's capital , have said their troops should not enter downtown Mogadishu , many are camped in the former American Embassy , a decrepit building that was closed more than 15 years ago after American soldiers suffered a humiliating defeat at the hands of warlords .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Ethiopia", "tail": "Addis Ababa"}, {"relation": "administrative division of country", "head": "Addis Ababa", "tail": "Ethiopia"}, {"relation": "country of capital", "head": "Ethiopia", "tail": "Addis Ababa"}, {"relation": "country of administrative divisions", "head": "Ethiopia", "tail": "Addis Ababa"}]


Instruction:
Text: Anti-Ethiopia riots erupted in Mogadishu , the capital of Somalia , on Friday , while masked gunmen emerged for the first time on the streets , a day after Ethiopian-backed troops captured the city from Islamist forces .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Somalia", "tail": "Mogadishu"}, {"relation": "country of capital", "head": "Somalia", "tail": "Mogadishu"}]


Instruction:
Text: Another judge told me that , given the shortcomings of the court , the government of Sudan , far from preventing the I.C.C. from taking over justice in Darfur , is all but ensuring that the I.C.C. prosecutor will come to Sudan .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "administrative division of country", "head": "Darfur", "tail": "Sudan"}, {"relation": "location contains", "head": "Sudan", "tail": "Darfur"}, {"relation": "administrative division of country", "head": "Darfur", "tail": "Sudan"}, {"relation": "country of administrative divisions", "head": "Sudan", "tail": "Darfur"}, {"relation": "location contains", "head": "Sudan", "tail": "Darfur"}, {"relation": "country of administrative divisions", "head": "Sudan", "tail": "Darfur"}]


Instruction:
Text: And now the International Criminal Court has begun its investigations into the mass murders and crimes against humanity that have been committed , and are still taking place , in the Darfur region of Sudan .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "country of administrative divisions", "head": "Sudan", "tail": "Darfur"}, {"relation": "location contains", "head": "Sudan", "tail": "Darfur"}, {"relation": "administrative division of country", "head": "Darfur", "tail": "Sudan"}]


Instruction:
Text: For now , Sudan has set up courts with an eye toward demonstrating that the Darfur cases are inadmissible to the International Criminal Court because they 're being handled perfectly well in Sudan .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "country of administrative divisions", "head": "Sudan", "tail": "Darfur"}, {"relation": "location contains", "head": "Sudan", "tail": "Darfur"}, {"relation": "administrative division of country", "head": "Darfur", "tail": "Sudan"}, {"relation": "location contains", "head": "Sudan", "tail": "Darfur"}, {"relation": "administrative division of country", "head": "Darfur", "tail": "Sudan"}, {"relation": "country of administrative divisions", "head": "Sudan", "tail": "Darfur"}]


Instruction:
Text: The State Department was working hard on Darfur even before Secretary of State Colin Powell declared in 2004 that Sudan 's actions in the region constituted genocide .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Sudan", "tail": "Darfur"}, {"relation": "administrative division of country", "head": "Darfur", "tail": "Sudan"}, {"relation": "country of administrative divisions", "head": "Sudan", "tail": "Darfur"}]


Instruction:
Text: Our hope is with the I.C.C. '' So , quietly and doggedly , Moreno-Ocampo is stitching together his file against the top leaders of the genocide in Darfur , collecting evidence from victims , activists and international officials , and perhaps this might , in a very partial way , help keep Sudan from falling apart .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "country of administrative divisions", "head": "Sudan", "tail": "Darfur"}, {"relation": "administrative division of country", "head": "Darfur", "tail": "Sudan"}, {"relation": "location contains", "head": "Sudan", "tail": "Darfur"}]


Instruction:
Text: In Darfur , as elsewhere in Sudan , the rebels are fighting their people 's economic and political marginalization .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "country of administrative divisions", "head": "Sudan", "tail": "Darfur"}, {"relation": "location contains", "head": "Sudan", "tail": "Darfur"}, {"relation": "administrative division of country", "head": "Darfur", "tail": "Sudan"}]


Instruction:
Text: Russia , another permanent member of the Security Council , has always been uneasy about the I.C.C. , given the continuing disaster in Chechnya .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "country of administrative divisions", "head": "Russia", "tail": "Chechnya"}, {"relation": "administrative division of country", "head": "Chechnya", "tail": "Russia"}, {"relation": "location contains", "head": "Russia", "tail": "Chechnya"}]


Instruction:
Text: In May 2002 , Under Secretary of State John R. Bolton sent a letter to Kofi Annan , secretary general of the United Nations , announcing that the United States did n't consider itself bound by the Rome Statute .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "person of company", "head": "Kofi Annan", "tail": "United Nations"}]


Instruction:
Text: News of the list 's existence unnerved officials in Khartoum , Sudan 's capital .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "country of capital", "head": "Sudan", "tail": "Khartoum"}, {"relation": "location contains", "head": "Sudan", "tail": "Khartoum"}]


Instruction:
Text: Luis Moreno-Ocampo was the assistant prosecutor and became a household name across Argentina .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "nationality", "head": "Luis Moreno-Ocampo", "tail": "Argentina"}]


Instruction:
Text: More than 50 public schools in nine Nassau County districts locked down their buildings yesterday as the police searched for three bank robbery suspects in the Baldwin area .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Nassau County", "tail": "Baldwin"}]


Instruction:
Text: His father retired as the manager of plumbing and heating at Manhattan College in Riverdale .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Riverdale", "tail": "Manhattan College"}]


Instruction:
Text: Lawrence O. Gostin , the chairman of the panel that conducted the study and a professor of law and public health at Georgetown University , said he hoped to change that .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "person of company", "head": "Lawrence O. Gostin", "tail": "Georgetown University"}]


Instruction:
Text: What happened at Holmesburg was just as gruesome as Tuskegee , but at Holmesburg it happened smack dab in the middle of a major city , not in some backwoods in Alabama , '' said Allen M. Hornblum , an urban studies professor at Temple University and the author of '' Acres of Skin , '' a 1998 book about the Holmesburg research . ''
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Alabama", "tail": "Tuskegee"}]


Instruction:
Text: I think we could have stood up stronger to the mayor and not bowed to this annual budget dance process that we go through , '' Councilman Charles Barron of Brooklyn said .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place lived", "head": "Charles Barron", "tail": "Brooklyn"}]


Instruction:
Text: When Flood was on the cusp of the majors , the Reds -- fearful , apparently , of having an all-black outfield -- traded him to St. Louis , a city and franchise with its own heavy racial baggage ; among other things , two Cardinals , Enos Slaughter and Joe Garagiola , had harassed Jackie Robinson when the Dodgers played there .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of birth", "head": "Joe Garagiola", "tail": "St. Louis"}, {"relation": "place lived", "head": "Joe Garagiola", "tail": "St. Louis"}]


Instruction:
Text: Marsha Lindsey , a black paramedic and former poll worker in Dayton , Ohio , said that after 2004 she stopped arguing with her black friends when they said there was no point in voting .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Ohio", "tail": "Dayton"}]


Instruction:
Text: Last weekend , Jim Webb , the Virginia Democrat who hopes to oust Senator George Allen , crammed in visits to 12 black churches , and for several weeks he has been pumping money into advertisements on black radio stations and in black newspapers .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place lived", "head": "Jim Webb", "tail": "Virginia"}, {"relation": "place lived", "head": "George Allen", "tail": "Virginia"}]


Instruction:
Text: In Tennessee , Representative Harold E. Ford Jr. is depending on a strong showing from blacks in Memphis , which he represents , to edge past Bob Corker and become the first black senator from a Southern state since Reconstruction .
【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 ['ethnicity', 'place lived', 'geographic distribution', 'company industry', 'country of administrative divisions', 'administrative division of country', 'location contains', 'person of company', 'profession', 'ethnicity of people', 'company shareholder among major shareholders', 'sports team of location', 'religion', 'neighborhood of', 'company major shareholders', 'place of death', 'nationality', 'children', 'company founders', 'company founded place', 'country of capital', 'company advisors', 'sports team location of teams', 'place of birth']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place lived", "head": "Bob Corker", "tail": "Tennessee"}, {"relation": "location contains", "head": "Tennessee", "tail": "Memphis"}]


Instruction:
Text: the most common audits were about waste and recycling .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "audits", "tail": "waste"}]


Instruction:
Text: this thesis defines the clinical characteristics of amyloid disease .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "thesis", "tail": "clinical characteristics"}]


Instruction:
Text: this outline focuses on spirituality , esotericism , mysticism , religion and/or parapsychology .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "outline", "tail": "spirituality"}]


Instruction:
Text: many of his literary pieces narrate and mention stories that took place in lipa .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "pieces", "tail": "stories"}]


Instruction:
Text: news programs commented on the violence from the game and expressed worries on how it would affect the players ' personalities .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "news programs", "tail": "violence"}]


Instruction:
Text: in the article , the authors explore the use of technology in small pharmacy chains .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "article", "tail": "use"}]


Instruction:
Text: the magazine was founded in order to keep athletes serving as soldiers informed about their sport back home .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "magazine", "tail": "sport"}]


Instruction:
Text: many films have portrayed mental illness or used it as a backdrop for other themes .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "films", "tail": "mental illness"}]


Instruction:
Text: this volume analyzes central concepts of the contemporary political 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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "volume", "tail": "concepts"}]


Instruction:
Text: the aim of this section is the interpretation of the results and their relation to the existing knowledge .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "section", "tail": "interpretation"}]


Instruction:
Text: david mamet took questions from the audience and shared his views on topics such as television , the democracy of culture and the english accent .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "views", "tail": "television"}]


Instruction:
Text: additionally , the chapter points out the importance of new employee orientation and lists some of the important things to cover during that process .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "chapter", "tail": "importance"}]


Instruction:
Text: the major theme of the book is the beauty of a dream .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "book", "tail": "beauty"}]


Instruction:
Text: his speech criticised the idea of religious education as a journey around the various religions .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "speech", "tail": "idea"}]


Instruction:
Text: he weaves complex tales concerning the romantic concerns of happiness , love and sufferance .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "tales", "tail": "concerns"}]


Instruction:
Text: several texts relate mythological anecdotes associated with his birth and exploits and explain his distinct iconography .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "texts", "tail": "anecdotes"}]


Instruction:
Text: these new documents point out a number of troubling strategies utilized by enron .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "documents", "tail": "strategies"}]


Instruction:
Text: the document is about illegal drugs , which , it claims , are illegal because they are harmful .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "document", "tail": "drugs"}]


Instruction:
Text: he has combined intelligent history with moving prose , creating an informative , inspiring narrative telling the history of a great people .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "narrative", "tail": "history"}]


Instruction:
Text: the second format is that of roundtables organised around keywords , that offer students the opportunity to participate in dialogue .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "roundtables", "tail": "keywords"}]


Instruction:
Text: he directed his criticism at media coverage of the catholic church .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "criticism", "tail": "media coverage"}]


Instruction:
Text: debate flourishes on the relative roles of the ancient greeks , mesopotamians and romans .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "debate", "tail": "roles"}]


Instruction:
Text: these investigations provided statistical evidence for genuine moral concern for the poor .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "investigations", "tail": "evidence"}]


Instruction:
Text: the book takes into account various phases of the life of the muhammad .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "book", "tail": "phases"}]


Instruction:
Text: the song basically criticizes the hypocrisy and lack of care from politicians .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "song", "tail": "hypocrisy"}]


Instruction:
Text: we are happy to relay the news that the various rights issues have been resolved .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "news", "tail": "rights issues"}]


Instruction:
Text: the new york times printed a story alleging the existence of a classified military space shuttle .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "story", "tail": "existence"}]


Instruction:
Text: the film revolves around a cadaver who seems to bring misfortune on those who come in contact with it .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "film", "tail": "cadaver"}]


Instruction:
Text: i welcome the news that a comprehensive agreement on bananas has now been reached .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "news", "tail": "agreement"}]


Instruction:
Text: i read the report from somalia on the agreement reached by faction leaders on the form of a future government that has been warmly welcomed .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "report", "tail": "agreement"}]


Instruction:
Text: this article explains the speech and language difficulties pre-school children encounter and how parents and early years workers can help .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "article", "tail": "difficulties"}]


Instruction:
Text: a panel discussion focusing on issues such as domestic violence and fearful relationships was held at the university of rio grande .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "discussion", "tail": "violence"}]


Instruction:
Text: however , only 20 research articles were identified on the topic of health promotion at retirement .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "articles", "tail": "promotion"}]


Instruction:
Text: at a recent meeting on the thorny subject of the gas trade between russia and ukraine , russian prime minister vladimir putin said mrs tymoshenko was a woman with whom he could do business .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "meeting", "tail": "trade"}]


Instruction:
Text: desperados is a cbbc children 's drama series following a wheelchair basketball team .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "drama series", "tail": "team"}]


Instruction:
Text: the shows were marred by some controversy in regard to tickets .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "controversy", "tail": "tickets"}]


Instruction:
Text: this document looks at the diversity of arts and creativity in rural england , and the key role that the arts play in rural regeneration .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "document", "tail": "diversity"}]


Instruction:
Text: a question was raised in regard to the public announcement of the names of the three finalists .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "question", "tail": "announcement"}]


Instruction:
Text: the movie featured a mutated sheep as the titular monster , which grew larger throughout the movie , until it exploded in the climactic scene .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "movie", "tail": "sheep"}]


Instruction:
Text: the first part gives an introduction to safety techniques , where risk analysis plays an important part .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "first part", "tail": "techniques"}]


Instruction:
Text: his paper narrated the discovery chronologically .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "paper", "tail": "discovery"}]


Instruction:
Text: the majority of the announced reforms had to do with changes in allocation mechanisms .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "reforms", "tail": "changes"}]


Instruction:
Text: we use newspaper advertising primarily to inform customers about new selections available .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "advertising", "tail": "selections"}]


Instruction:
Text: therefore , we have made a new 8-page leaflet informing about all the qualities of our large range of pressure filters .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "leaflet", "tail": "qualities"}]


Instruction:
Text: the newsletter is published once a fortnight to keep families informed on school happenings .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "newsletter", "tail": "happenings"}]


Instruction:
Text: however , there are many universal humanitarian laws applying to war .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "laws", "tail": "war"}]


Instruction:
Text: discussion took place on the function of this committee .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "discussion", "tail": "function"}]


Instruction:
Text: it is a formal legal document that sets out the facts and legal reasons .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "document", "tail": "facts"}]


Instruction:
Text: the document established levels of voltage distortion acceptable to the distribution system .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "document", "tail": "levels"}]


Instruction:
Text: also in the programme we consider the subject of happiness .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "programme", "tail": "happiness"}]


Instruction:
Text: the internal memo gives details of morgan stanley 's new management lineup .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "memo", "tail": "lineup"}]


Instruction:
Text: the u.s. transportation security administration ( tsa ) has lost a hard drive containing information about 100,000 former and current employees .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "information", "tail": "employees"}]


Instruction:
Text: the swedish society for eighteenth century studies is an interdisciplinary forum for researchers specialized in eighteenth century .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "forum", "tail": "eighteenth century"}]


Instruction:
Text: robert redford 's historical drama about the aftermath of the assassination of abraham lincoln looks set to get under way .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "drama", "tail": "aftermath"}]


Instruction:
Text: the paper includes both an essay surveying the literature and a descriptor-indexed bibliography of over 200 papers and books .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "essay", "tail": "literature"}]


Instruction:
Text: this page explains commonly used terms in chess in alphabetical order .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "page", "tail": "terms"}]


Instruction:
Text: at the seminar , we gave an account of the current education system and teacher education environment in hong kong .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "seminar", "tail": "system"}]


Instruction:
Text: a new regulation governing the access to documents in the custody of national archives came into effect on the 26th of february 2003 .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "regulation", "tail": "access"}]


Instruction:
Text: this paper thus presents a detailed review of previous work that evaluates the optimum operational decisions of an lse .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "paper", "tail": "review"}]


Instruction:
Text: the journal takes an inter-disciplinary approach , examining the history , culture and theory of anarchism .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "journal", "tail": "history"}]


Instruction:
Text: the display showing signal and battery status also incorporates feedback from machinery being operated .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "display", "tail": "status"}]


Instruction:
Text: one chapter of the book relates attempts made by the us to establish a similar alliance with yemen .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "chapter", "tail": "attempts"}]


Instruction:
Text: the fairy tale describes a giant with a pair of magic boots that allow the wearer to cover seven leagues in one stride .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "fairy tale", "tail": "giant"}]


Instruction:
Text: this table serves as an example of the data and observations routinely collected in the horner rootstock trial .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "table", "tail": "data"}]


Instruction:
Text: general contract law issues are examined , as is the law covering the registration of hotel guests .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "law", "tail": "registration"}]


Instruction:
Text: patriarch cerularius eventually wrote a tract against the western liturgical practices .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "tract", "tail": "liturgical practices"}]


Instruction:
Text: gatrell illuminates the debate over public execution that raged in polite society .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "debate", "tail": "public execution"}]


Instruction:
Text: you have been asked to make notes about a telephone call left by a colleague in the german office of the 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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "notes", "tail": "telephone call"}]


Instruction:
Text: questions on various aspects of the study were raised and discussed .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "questions", "tail": "aspects"}]


Instruction:
Text: he is the author of eight books which have charted the transformation of europe over the last quarter-century .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "books", "tail": "transformation"}]


Instruction:
Text: the story portrays a boy who encounters a young woman whom he must serve .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "story", "tail": "boy"}]


Instruction:
Text: our whole evening was centered on the topic of courage .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "evening", "tail": "courage"}]


Instruction:
Text: this documentary film reveals a shocking insight into the jamaican economy and everyday 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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "film", "tail": "economy"}]


Instruction:
Text: the focus of the chapter is the impact of diversification on the performance of pe funds .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "chapter", "tail": "impact"}]


Instruction:
Text: the commission has published an annual report giving a summary of committee activities during the previous 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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "report", "tail": "activities"}]


Instruction:
Text: typically , statutes declare policy .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "statutes", "tail": "policy"}]


Instruction:
Text: the bulletin informed key persons and opinion leaders about the project , its background , objectives , activities , and results .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "bulletin", "tail": "project"}]


Instruction:
Text: the book introduces modern geographical theory in an accessible format and reflects the changing nature of the subject .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "book", "tail": "geographical theory"}]


Instruction:
Text: preliminary talks between the association and nigel worthington regarding a new contract took place last week and these talks are still ongoing .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "talks", "tail": "contract"}]


Instruction:
Text: the weather report informed me about the high wind storm conditions that was already in place .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "weather report", "tail": "storm conditions"}]


Instruction:
Text: it has been asserted in the literature that the recognition of speech takes place not because of phonemic distinctions only .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "literature", "tail": "recognition"}]


Instruction:
Text: the second lesson points to the predominant role of quality over quantity .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "lesson", "tail": "predominant"}]


Instruction:
Text: the report provides a full account of the societal impacts of car production .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "report", "tail": "societal impacts"}]


Instruction:
Text: the icon is generally a flat panel painting depicting a holy being or object .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "painting", "tail": "being"}]


Instruction:
Text: one of the few clear and non-controversial norms governing issues of state succession are the customary rules enshrined in article 11 .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "norms", "tail": "issues"}]


Instruction:
Text: the controversy has centered mainly on intelligence .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "controversy", "tail": "intelligence"}]


Instruction:
Text: this blog tracks and comments on news relevant to the interplay of science & religion .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "news", "tail": "interplay"}]


Instruction:
Text: below you find my ideas with regard to this issue .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "ideas", "tail": "issue"}]


Instruction:
Text: the second hypothesis asserting economic domination is rejected , and the third largely accepted .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "hypothesis", "tail": "domination"}]


Instruction:
Text: this volume synthesizes current understanding of the ecology of alaska 's boreal forests .
【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 ['component whole', 'NA', 'instrument agency', 'member collection', 'cause effect', 'entity destination', 'content container', 'message topic', 'product producer', 'entity origin']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "message topic", "head": "volume", "tail": "understanding"}]