Instruction:
Text:  Russia 's lower and upper houses of Parliament called on President Vladimir V. Putin to sever relations or to impose sanctions on Estonia , which was part of the Soviet Union from its absorption during World War II until its independence in 1991 .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

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


Instruction:
Text:  In 2004 , the last major flood , the death toll stood at 351 in Bihar state , which is home to this village and many others sitting on some of the most vulnerable floodplains in India .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

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


Instruction:
Text:  It surveys the revolutionary work he shot in Spain , Italy , Mexico , Britain and France .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

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


Instruction:
Text:  Claudia is one of about 1,500 Jews who live in Cuba ; 1,100 reside in Havana , and the remaining 400 are spread among the provinces .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "administrative division of country", "head": "Havana", "tail": "Cuba"}]


Instruction:
Text:  It 's even better when you can just roll out of bed each morning and tackle a different menu , spending one day on Neapolitan-style pizza and perhaps returning on another for pasta , as you can at the Sorrento Cooking School at the Esperidi Resort on the Amalfi Coast in Italy .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Italy", "tail": "Amalfi Coast"}]


Instruction:
Text:  Her life began in Munich , Germany in 1916 .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

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


Instruction:
Text:  Let 's see , Syria is being investigated by the United Nations for murdering Lebanon 's former prime minister , Rafik Hariri .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "nationality", "head": "Rafik Hariri", "tail": "Lebanon"}]


Instruction:
Text:  Now , the state is retooling the program to include all of Cook County , which encompasses Chicago and many of its suburbs .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Cook County", "tail": "Chicago"}]


Instruction:
Text:  The bride , 26 , is a first-year resident in anatomic and clinical pathology at Johns Hopkins Hospital in Baltimore .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Baltimore", "tail": "Johns Hopkins Hospital"}]


Instruction:
Text:  Mes3n Sacrist\u00eda de Capuchinas , Puebla , Mexico , 800-728-9098 ; www.mexicoboutiquehotels.com/capuchinas .\r\n
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

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


Instruction:
Text:  In California , where parents first started educational foundations in response to a statewide law capping property taxes , the combined district of Santa Monica and Malibu requires that 15 percent of the gifts from parents to individual schools must go in an '' equity fund '' that is administered by an independent foundation .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "California", "tail": "Malibu"}]


Instruction:
Text:  Lower-level prisoners from Afghanistan and Iraq were held at a military base near the Black Sea in Romania , the report says .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Romania", "tail": "Black Sea"}]


Instruction:
Text:  In addition , a second new indictment charged Mr. Hindi and Ashraf Zaim , 39 , of Ottawa Hills , another town in the Toledo area , with defrauding the government by claiming to have used $ 40,000 in federal grant money for charitable purposes when in fact , the government said , it actually went to their personal use .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Toledo", "tail": "Ottawa Hills"}]


Instruction:
Text:  Senator Gordon H. Smith of Oregon started the trend late last year , followed by others , including Senator Norm Coleman of Minnesota last week .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place lived", "head": "Norm Coleman", "tail": "Minnesota"}]


Instruction:
Text:  He is survived by his wife , Linda , and daughters , Sharon Kofmehl of Charleston , SC , and Sandy Kofmehl of Paris , France , and granddaughter , Emma Kofmehl of Charleston .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "France", "tail": "Paris"}]


Instruction:
Text:  In New York , these Spurs would have become the toast of the United Nations with their rich blend of international stars , beginning but not ending with Duncan of the United States Virgin Islands , Gin3bili of Argentina and Tony Parker of France .\r\n
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "nationality", "head": "Tony Parker", "tail": "France"}]


Instruction:
Text:  The museum , which opened in the North Beach neighborhood of San Francisco last fall , has made plans to sponsor a spring tour of Mr. Morgan 's space scrap in a vintage Airstream trailer , creating a kind of Electric Kool-Aid Acid Test for the astronomy set .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "San Francisco", "tail": "North Beach"}]


Instruction:
Text:  Behind the News The suicide attacks in Algiers , the capital , were the deadliest in more than a decade and followed warnings by officials in the United States and Europe that Algeria was becoming a center for terrorism that could threaten Europe and North America .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "country capital", "head": "Algeria", "tail": "Algiers"}]


Instruction:
Text:  And Malone 's pharmacy , which had '' looked like a mini-Wal-Mart '' was rebuilt '' with an old soda fountain and a nice little restaurant that made it like an old-fashioned drugstore , '' Mr. Nix said , adding that the redevelopment '' made Arkadelphia more viable than a lot of other Arkansas downtowns . ''
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Arkansas", "tail": "Arkadelphia"}]


Instruction:
Text:  ` STRANGE DAYS ON PLANET EARTH : THE ONE DEGREE FACTOR , ' New York Aquarium , Surf Avenue and West Eighth Street , Coney Island , Brooklyn , (718) 265-3474 .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Coney Island", "tail": "New York Aquarium"}]


Instruction:
Text:  The bishops were met by Israel 's deputy prime minister , Shimon Peres .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "nationality", "head": "Shimon Peres", "tail": "Israel"}]


Instruction:
Text:  CARVING THE GRANITE A Park as Eight Miles of Exits The Flume , Cannon Mountain and the Old Man of the Mountain Historic Site are all part of Franconia Notch State Park www.franconianotchstatepark.com in New Hampshire .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "New Hampshire", "tail": "Cannon Mountain"}]


Instruction:
Text:  Both Russia and China crossed major diplomatic thresholds last summer when they agreed to seek Security Council sanctions against Iran , but both countries have also dragged their feet about actually imposing them .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

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


Instruction:
Text:  He turned for guidance to Daniel Pauly , director of the fisheries center at the University of British Columbia , which maintains an elaborate global database on fishing .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "company", "head": "Daniel Pauly", "tail": "University of British Columbia"}]


Instruction:
Text:  Sheldon Whitehouse , Democrat of Rhode Island , and Claire McCaskill , Democrat of Missouri , both former prosecutors , were especially eloquent about the way in which Mr. Gonzales has betrayed the ideals of American law .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place lived", "head": "Sheldon Whitehouse", "tail": "Rhode Island"}]


Instruction:
Text:  THOUGH the regions of Mexico retain their very distinct cuisines , Mexico City acts like a food processor , pur9eing ingredients until you ca n't tell what went into the final mix .\r\n
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "administrative division of country", "head": "Mexico City", "tail": "Mexico"}]


Instruction:
Text:  An Apparently Unimportant Event : Self-Taught from the Centre d'Art in the 1940s and 50s , '' Kresge and Pascal galleries , Berrie Center , Ramapo College , 505 Ramapo Valley Road , Mahwah , through March 7 .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "location contains", "head": "Mahwah", "tail": "Ramapo College"}]


Instruction:
Text:  Webkinz , which are manufactured by the privately held Ganz Corporation in Woodbridge , Ontario , break that mold by existing simultaneously in the child 's room and on Webkinz World Webkinz.com .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

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


Instruction:
Text:  Except for sociopaths , humans are built to feel and act out of empathy , said Stephen G. Post , a professor of bioethics at Case Western Reserve University 's medical school and co-author of '' Why Good Things Happen to Good People , '' scheduled to be published in May .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "company", "head": "Stephen G. Post", "tail": "Case Western Reserve University"}]


Instruction:
Text:  Unlike previous Mexico City alternative rock radio ventures , Reactor made breaking independent and underground bands from Mexico as central to its mission as playing the latest from Franz Ferdinand . ''
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['nationality', 'country capital', 'place of death', 'children', 'location contains', 'place of birth', 'place lived', 'administrative division of country', 'country of administrative divisions', 'company', 'neighborhood of', 'company founders']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "administrative division of country", "head": "Mexico City", "tail": "Mexico"}]


Instruction:
Text: But that spasm of irritation by a master intimidator was minor compared with what Bobby Fischer , the erratic former world chess champion , dished out in March at a news conference in Reykjavik , Iceland .
【Relation 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": "Bobby Fischer", "tail": "Iceland"}, {"relation": "country of capital", "head": "Iceland", "tail": "Reykjavik"}, {"relation": "location contains", "head": "Iceland", "tail": "Reykjavik"}, {"relation": "place of death", "head": "Bobby Fischer", "tail": "Reykjavik"}]


Instruction:
Text: But Schaap seems as comfortable in that role as Joe Buck , the Fox baseball and football sportscaster who so clearly benefited from learning beside his father , Jack Buck , the late voice of the St. Louis Cardinals . ''
【Relation 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": "children", "head": "Jack Buck", "tail": "Joe Buck"}]


Instruction:
Text: www.bonhams.com July 22 Mecum Hawkeye Classic Auction , Iowa Sate Fairgrounds , Des Moines .
【Relation 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": "Iowa", "tail": "Des Moines"}]


Instruction:
Text: www.formula1.com August Aug. 1-5 National Corvette Restorers Society Annual Convention , Henry B. Gonzalez Convention Center , San Antonio .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['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": "Henry B. Gonzalez", "tail": "San Antonio"}, {"relation": "place lived", "head": "Henry B. Gonzalez", "tail": "San Antonio"}, {"relation": "place of death", "head": "Henry B. Gonzalez", "tail": "San Antonio"}]


Instruction:
Text: (248) 269-7672 , www.meadowbrookconcours.org Aug. 6 Formula One Grand Prix , Budapest , Hungary .
【Relation 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": "Hungary", "tail": "Budapest"}, {"relation": "country of capital", "head": "Hungary", "tail": "Budapest"}, {"relation": "administrative division of country", "head": "Budapest", "tail": "Hungary"}, {"relation": "location contains", "head": "Hungary", "tail": "Budapest"}]


Instruction:
Text: www.lemansclassic.com July 9 Italian Car Festival , Stark County Fairgrounds , Canton , Ohio .
【Relation 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": "Canton"}]


Instruction:
Text: Hockenheim , Germany .
【Relation 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": "Germany", "tail": "Hockenheim"}]


Instruction:
Text: The final deal was brokered through the major assistance of Annette L. Nazareth , an S.E.C. commissioner who once led its market regulation office , and Frank G. Zarb , the former chairman of NASD and a major presence on Wall Street and in Washington for much of his career .
【Relation 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": "Frank G. Zarb", "tail": "NASD"}]


Instruction:
Text: Mary L. Schapiro , who earlier this year became the new head of NASD , was more amenable to fashioning a deal to the New York Exchange 's liking than her predecessor , Robert R. Glauber .
【Relation 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": "Robert R. Glauber", "tail": "NASD"}]


Instruction:
Text: Graveside service Monday January 31 , 2:00 P.M. at Riverside Cemetery , Rochelle Park , N.J. Donations may be made to Hospice By The Sea , Boca Raton , Florida .
【Relation 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": "Florida", "tail": "Boca Raton"}]


Instruction:
Text: This summer , the United States Embassy in Beirut , Lebanon , once again made its presence felt on the cultural scene by sponsoring a photo exhibition , an experimental jazz performance , a classical music concert and a visit from the Whiffenpoofs , Yale University 's a cappella singers .
【Relation 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": "Lebanon", "tail": "Beirut"}, {"relation": "country of capital", "head": "Lebanon", "tail": "Beirut"}]


Instruction:
Text: Sophiline Shapiro , the Cambodian-born artistic director of the Khmer Arts Academy , which has a school in Long Beach , Calif. , and a new dance company in Cambodia , was one of the first dance students at the School of Fine Arts in Phnom Penh after the fall of the Khmer Rouge in 1979 .
【Relation 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": "Phnom Penh", "tail": "Cambodia"}, {"relation": "country of capital", "head": "Cambodia", "tail": "Phnom Penh"}, {"relation": "country of administrative divisions", "head": "Cambodia", "tail": "Phnom Penh"}, {"relation": "location contains", "head": "Cambodia", "tail": "Phnom Penh"}]


Instruction:
Text: Homage to Cambodia '' was performed at Chaktomuk Conference Hall in Phnom Penh on Oct. 21 , attended by the king .
【Relation 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": "Cambodia", "tail": "Phnom Penh"}, {"relation": "administrative division of country", "head": "Phnom Penh", "tail": "Cambodia"}, {"relation": "country of administrative divisions", "head": "Cambodia", "tail": "Phnom Penh"}, {"relation": "location contains", "head": "Cambodia", "tail": "Phnom Penh"}]


Instruction:
Text: Mr. Hollander began touring in Asia in the early 1990 's , when he was a Fulbright lecturer in India , talking about dance and touring with his company .
【Relation 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": "Asia", "tail": "India"}]


Instruction:
Text: The proportion of residents in the town of East Hampton who are 65 and older , 16.6 percent , is one of the highest in Suffolk County , according to a draft of the regional planning board 's report .
【Relation 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": "Suffolk County", "tail": "East Hampton"}]


Instruction:
Text: In Yaphank , the report notes , the Suffolk County executive , Steve Levy , has proposed a development on county-owned land that would have up to 1,000 residential units of '' employer-assisted housing , '' referring to a housing assistance program that combines employer contributions with state and federal 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 ['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": "Suffolk County", "tail": "Yaphank"}]


Instruction:
Text: Born in Montreal , resided in Middle Village and Bayside , Queens for 70 years and Deerfield Beach , FL for 30 years .
【Relation 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": "Queens", "tail": "Bayside"}, {"relation": "location contains", "head": "Queens", "tail": "Middle Village"}, {"relation": "neighborhood of", "head": "Middle Village", "tail": "Queens"}, {"relation": "neighborhood of", "head": "Bayside", "tail": "Queens"}]


Instruction:
Text: As a half Englishman , half Frenchman who arrived in New York from Paris in 1965 at 23 , and has opened and operated fashion boutiques , night clubs and now restaurants , including La Goulue on Madison Avenue , Mr. Denoyer has served the moneyed men and women of Manhattan well , putting them at ease on their own terms .
【Relation 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": "Manhattan"}, {"relation": "place lived", "head": "La Goulue", "tail": "Paris"}]


Instruction:
Text: The couple have two daughters , Seon-yong , 34 , who works in Seoul for the Korea Foundation , a nonprofit organization that promotes Korean culture , and Hyun-hee , 30 , a field officer for Unicef in Nairobi , Kenya .
【Relation 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": "Kenya", "tail": "Nairobi"}, {"relation": "country of capital", "head": "Kenya", "tail": "Nairobi"}]


Instruction:
Text: WHERE -- Dixie , Idaho WHAT -- 2-bedroom house HOW MUCH $ 285,000 This 1,400-square-foot log house is on 10 acres bordered on three sides by the Nez Perce National Forest .
【Relation 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": "Idaho", "tail": "Nez Perce National Forest"}]


Instruction:
Text: WHERE -- Honey Harbour , Ontario WHAT -- 2-bedroom cabin HOW MUCH -- $ 160,000 This cabin is on a Georgian Bay island , part of which is federal park land .
【Relation 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": "Ontario", "tail": "Georgian Bay"}]


Instruction:
Text: May is a former teammate of Hideki Matsui with the Yomiuri Giants in Japan in 2000 and 2001 .
【Relation 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": "Hideki Matsui", "tail": "Japan"}]


Instruction:
Text: Over the last decade , when it came to choosing who would represent Staten Island and southwest Brooklyn in Congress , Anita Lerman has been the unchallenged standard-bearer of the Independence Party , a small but growing group with 6,703 members on the island .
【Relation 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": "Anita Lerman", "tail": "Brooklyn"}]


Instruction:
Text: Resident of Roslyn , 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": "Roslyn"}]


Instruction:
Text: Ohio Coach Frank Solich said : '' This would not have made our season if we won , and it did not break our season in losing 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 ['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": "Frank Solich", "tail": "Ohio"}]


Instruction:
Text: There were two people in the courtroom who looked up to the German-born judges as the best of Germany and looked down on the prosecutor as a miserable Ostjude : one was Eichmann and the other was Hannah Arendt . ''
【Relation 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": "Hannah Arendt", "tail": "Germany"}]


Instruction:
Text: Defense Secretary Donald H. Rumsfeld spent Tuesday in a whirlwind trip around Iraq that included '' town hall '' meetings with American troops outside the capital , talks with government officials in Baghdad , and a final stop here , at a Kurdish stronghold beneath snow-capped mountains where anti-Saddam Hussein forces plotted for years against the Iraqi dictator -- and against other Kurds .
【Relation 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": "Baghdad"}, {"relation": "country of capital", "head": "Iraq", "tail": "Baghdad"}]


Instruction:
Text: Crucial to the team 's success was the recruitment of an international roster of stars , led by the exuberant Pele -LRB- coaxed out of retirement with the help of Secretary of State Henry Kissinger and untold millions -RRB- and Germany 's smooth-as-silk Franz Beckenbauer .
【Relation 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": "Henry Kissinger", "tail": "Germany"}, {"relation": "nationality", "head": "Franz Beckenbauer", "tail": "Germany"}]


Instruction:
Text: An article on Thursday about the spread of electrical failures and transportation service cutbacks in the New York City area misspelled the surname of a Queens woman whose 12-story apartment building lost power on Wednesday morning .
【Relation 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": "Queens"}]


Instruction:
Text: The comedian John Leguizamo , who was born in Colombia and grew up in Jackson Heights , Queens , played himself in a radio spot on the sports station WFAN in which he left a series of increasingly desperate phone messages begging Minaya for 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 ['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": "John Leguizamo", "tail": "Queens"}, {"relation": "neighborhood of", "head": "Jackson Heights", "tail": "Queens"}, {"relation": "location contains", "head": "Queens", "tail": "Jackson Heights"}]


Instruction:
Text: He 's got the tools , and he 's a great kid , but you ca n't expect him to be fielding like Omar Vizquel '' -- the Giants ' slick-fielding shortstop from Caracas .
【Relation 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": "Omar Vizquel", "tail": "Caracas"}, {"relation": "place lived", "head": "Omar Vizquel", "tail": "Caracas"}]


Instruction:
Text: You Ca n't Walk Your Way Off the Island The architect of the New Mets , Omar Minaya , was 8 when his family moved to Corona , Queens , from the Dominican Republic in 1967 , a little more than five years after the assassination of the dictator Rafael Trujillo .
【Relation 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": "Omar Minaya", "tail": "Queens"}]


Instruction:
Text: According to the city 's Economic Development Corporation , the Brooklyn terminal will accommodate 192,000 passengers from 38 ships in its first year of operation , eventually accounting for about one-fifth of the roughly one million passengers who will pass through New York City this 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 ['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: I hate to rush this press conference , actually , but I heard the shuffleboard tournament starts at 11 , and Marty thinks that he can take me , '' said Mayor Michael R. Bloomberg , referring to the Brooklyn borough president , Marty Markowitz . ''
【Relation 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": "Marty Markowitz", "tail": "Brooklyn"}, {"relation": "place lived", "head": "Marty Markowitz", "tail": "Brooklyn"}]


Instruction:
Text: I 3\/5sheart 4\/5 $ '' (1986) is , in the broadest sense , about the circulation of money : in its 140 minutes it visits a hectic foreign exchange bureau in Amsterdam ; private bankers on Wall Street ; a racetrack in Hong Kong ; a Puerto Rican mother and her son struggling to set up a restaurant along Avenue C , then devastated , in the East Village ; and a raucous farmers ' market back in the Netherlands .
【Relation 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": "Netherlands", "tail": "Amsterdam"}, {"relation": "location contains", "head": "Netherlands", "tail": "Amsterdam"}]


Instruction:
Text: Lubitsch 's Hollywood Europe was a highly artificial , thoroughly imagined place , but then so was the Europe he envisioned while still in Germany , as revealed by the five wonderful films being released today on four discs by Kino International under the series title '' Lubitsch in Berlin . ''
【Relation 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": "Germany", "tail": "Berlin"}, {"relation": "location contains", "head": "Europe", "tail": "Germany"}, {"relation": "country of capital", "head": "Germany", "tail": "Berlin"}, {"relation": "country of administrative divisions", "head": "Germany", "tail": "Berlin"}, {"relation": "location contains", "head": "Europe", "tail": "Berlin"}, {"relation": "administrative division of country", "head": "Berlin", "tail": "Germany"}]


Instruction:
Text: Lubitsch in Berlin The director Ernst Lubitsch is best known for the continental fantasies he created during his tenure at Paramount in Hollywood , films like '' Trouble in Paradise '' (1932) and '' Ninotchka '' (1939) , which took place in a Paris of infinite elegance , wit and sexual tolerance .
【Relation 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": "Ernst Lubitsch", "tail": "Berlin"}, {"relation": "place of birth", "head": "Ernst Lubitsch", "tail": "Berlin"}, {"relation": "place of death", "head": "Ernst Lubitsch", "tail": "Hollywood"}]


Instruction:
Text: YouTube 's bite also hurt Senator Joseph I. Lieberman , who was defeated by the political upstart Ned Lamont in Connecticut 's Democratic primary earlier this month .
【Relation 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": "Ned Lamont", "tail": "Connecticut"}]


Instruction:
Text: Last week , Senator George Allen , the Virginia Republican , was caught on tape at a campaign event twice calling a college student of Indian descent a '' macaca , '' an obscure racial slur .
【Relation 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": "George Allen", "tail": "Virginia"}]


Instruction:
Text: Iran allowed inspectors to take environmental samples this week from the site , at Parchin , southeast of Tehran .
【Relation 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": "Iran", "tail": "Tehran"}, {"relation": "country of capital", "head": "Iran", "tail": "Tehran"}]


Instruction:
Text: It has two other stores in Hudson County -- in Jersey City and Secaucus -- and a third is being constructed in North Bergen on Tonnelle Avenue .
【Relation 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": "Hudson County", "tail": "Jersey City"}, {"relation": "location contains", "head": "Hudson County", "tail": "North Bergen"}, {"relation": "location contains", "head": "Hudson County", "tail": "Secaucus"}]


Instruction:
Text: The zoning statute that governs the redevelopment around the Holland Tunnel makes no stipulation for traffic , so Jersey City can not reject the plan based on traffic alone , said Kristin Russell , a city planner .
【Relation 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": "Jersey City", "tail": "Holland Tunnel"}]


Instruction:
Text: Kyoto 's cuisine -LRB- kyo-ryori -RRB- is the legacy of court and temple , aristocratic and understated ; unusual locations and exquisite food made the meals I ate there some of the most magical of my life , and made the traditional Japan I 'd first fallen in love with palpable .
【Relation 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": "Japan", "tail": "Kyoto"}]


Instruction:
Text: Yoshi Tsuji runs several cooking schools in Japan and in Europe ; Kazuki Kondo , dean of the Osaka school , joined us for dinner .
【Relation 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": "Japan", "tail": "Osaka"}]


Instruction:
Text: A century ago , the greatest playground was Coney Island , a pre-Disney seaside place to cavort at the southernmost edge of Brooklyn .
【Relation 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": "neighborhood of", "head": "Coney Island", "tail": "Brooklyn"}, {"relation": "location contains", "head": "Brooklyn", "tail": "Coney Island"}]


Instruction:
Text: Even wishful thinkers do n't believe she can medal here , much less win , but who would have wageredon Dan Jansen before he ended his long Olympic odyssey by winning a speedskating gold medal for his dead sister in 1994 in Lillehammer , Norway ?
【Relation 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": "Norway", "tail": "Lillehammer"}]


Instruction:
Text: OVER the last few years , as its residents fought a battle against the city to keep their homes , the Fort Trumbull neighborhood of New London looked more and more like a war-torn village , with acres of vacant lots interrupted intermittently by debris and a few scattered houses .
【Relation 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 London", "tail": "Fort Trumbull"}]


Instruction:
Text: Curry and Crawford , close friends and longtime teammates in Chicago and New York , were in sync in the final quarter .
【Relation 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": "Crawford"}]


Instruction:
Text: The Knicks were 10 for 15 from the free-throw line in the fourth quarter , but had only 2 turnovers , and Coach Isiah Thomas called it a vast improvement from their fourth quarter in Chicago . ''
【Relation 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": "Isiah Thomas", "tail": "Chicago"}]


Instruction:
Text: Nanette Lepore , a New York fashion designer , said she reserved a seat every week to go to her home in Amagansett because she '' ca n't bear standing , especially when I 've worked all day and I 'm stressed out . ''
【Relation 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": "Amagansett"}]


Instruction:
Text: Senators Jon Corzine , Democrat of New Jersey , and Susan Collins , Republican of Maine , have been working hard on the 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 ['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": "Susan Collins", "tail": "Maine"}]


Instruction:
Text: OVED-WEISS -- Or , 23 , beloved son of Inbal Oved and Mark and Cathy Weiss , died suddenly and tragically in Bangkok , Thailand .
【Relation 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": "Thailand", "tail": "Bangkok"}, {"relation": "country of capital", "head": "Thailand", "tail": "Bangkok"}, {"relation": "location contains", "head": "Thailand", "tail": "Bangkok"}, {"relation": "administrative division of country", "head": "Bangkok", "tail": "Thailand"}]


Instruction:
Text: It envisioned a sprawling cultural center and Fordham campus on the squalid West Side , a civic plaza in Downtown Brooklyn that Robert Moses promised would rival the Piazza San Marco in Venice , a grand development over the Sunnyside railroad yards in Queens , and a Palace of Progress devoted to world trade atop a reconstructed Pennsylvania Station .
【Relation 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": "Venice", "tail": "Piazza San Marco"}]


Instruction:
Text: Scott Schwind has joined Fulbright & Jaworski as an energy partner in Houston .
【Relation 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": "company founded place", "head": "Fulbright & Jaworski", "tail": "Houston"}]


Instruction:
Text: He writes that the State Department 's Future of Iraq project was sidelined because of tensions between the State Department and the Pentagon , and that its coordinator , Tom Warrick , '' who had done as much thinking about postwar Iraq as any American official '' also '' became a casualty of the interagency war and did n't get to Baghdad for a 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 ['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": "Baghdad"}, {"relation": "country of capital", "head": "Iraq", "tail": "Baghdad"}, {"relation": "location contains", "head": "Iraq", "tail": "Baghdad"}, {"relation": "country of capital", "head": "Iraq", "tail": "Baghdad"}]


Instruction:
Text: A more recent list included references to Wiener Werkstatte designs , Los Angeles graffiti , a Buddha fountain in Chinatown , a book by Aldous Huxley and an action figure of Ginger Lynn , the pornography star .
【Relation 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 death", "head": "Aldous Huxley", "tail": "Los Angeles"}]


Instruction:
Text: He has been through four uniform changes , has seen the Angels name changed from California to Anaheim to the Los Angeles Angels of Anaheim .
【Relation 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": "California", "tail": "Anaheim"}, {"relation": "location contains", "head": "California", "tail": "Anaheim"}]


Instruction:
Text: Like Atlanta 's John Smoltz -LRB- 17 seasons -RRB- and the Astros ' Craig Biggio -LRB- 18 seasons -RRB- , Anderson is a franchise lifer .
【Relation 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": "John Smoltz", "tail": "Atlanta"}]


Instruction:
Text: Nelson McCausland , a Belfast politician and a member of the Democratic Unionists , a hard-line Protestant party , said : '' There is a fundamental anger and alienation .
【Relation 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": "Nelson McCausland", "tail": "Belfast"}]


Instruction:
Text: The Bush administration , condemning the assassination of the former prime minister , Rafik Hariri , in Lebanon , suggested Monday that Syria was to blame and moved to get a new condemnation of Syria 's domination of Lebanon at the United Nations Security Council .
【Relation 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": "Rafik Hariri", "tail": "Lebanon"}, {"relation": "nationality", "head": "Rafik Hariri", "tail": "Lebanon"}]


Instruction:
Text: American officials said the killing was an ominous development on two counts : first , because it raised concern that Lebanon could plunge back into the civil war that it suffered throughout the 1980 's , and second , because it underscored growing American impatience with the role played by Syria in the Middle East .
【Relation 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": "Middle East", "tail": "Syria"}, {"relation": "location contains", "head": "Middle East", "tail": "Lebanon"}]


Instruction:
Text: And in fact the Syrian foreign minister , Farouk al-Sharaa , speaking at a news conference in Damascus , also condemned the attack .
【Relation 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": "Farouk al-Sharaa", "tail": "Damascus"}]


Instruction:
Text: Mr. McClellan and other administration spokesmen said they had no concrete evidence of Syria 's involvement in the killing of Mr. Hariri , a prominent opposition leader and critic of Syria 's role in Lebanon , who died along with at least 11 others when a car bomb blew up next to his motorcade in Beirut .
【Relation 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": "Lebanon", "tail": "Beirut"}, {"relation": "location contains", "head": "Lebanon", "tail": "Beirut"}]


Instruction:
Text: In 1999 one of its members , Evan Ziporyn , a composer and clarinetist and a professor at the Massachusetts Institute of Technology , met Kyaw Kyaw Naing , an expatriate Burmese percussion virtuoso now living in New York , at a concert in suburban Boston .
【Relation 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": "Evan Ziporyn", "tail": "Massachusetts Institute of Technology"}]