Instruction:
Text: Cole was born at Portsmouth, the son City Of Portsmouth the landscape painter, George Vicat Cole Cole (1810-1883), and in his practice followed his father's lead with marked success. He exhibited at the British Institution at the age of nineteen, and was first represented at the Royal Academy in 1853. His election as an associate of this institution took place in 1870, and he became an Academician ten years later. He died in London on the 6th of April 1893. The wide popularity of his work was due partly to the simple directness of his technical method, and partly to his habitual choice of attractive material.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of birth", "head": "George Vicat Cole", "tail": "City Of Portsmouth"}]


Instruction:
Text: this gorgeous woman here, Kathy Tom Freston , who was vetted by Arianna. It took five years and swimming upstream, but now the love Bachelor of Arts my life is standing here next to me. The crowd aahhed. Kathy and Dan have been dating about a year, his bubbly mom, Dolly Buettner, told me. In a private moment, Buettner told me, You have got the scoop.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education degree", "head": "Tom Freston", "tail": "Bachelor of Arts"}]


Instruction:
Text: He also advises pu Robert B lic company directors and management on specific disclosure and corporate governance issues, including Sarbanes-Oxley and Dodd-Frank Act compliance, takeover defenses, shareholder relations, stock exchange listing matters, and general state law fiduciary duty issues. Before entering private practice, Mr. Murphy was an Attorney-Adviser for the SEC is Division of Corporation Finance in Washington , D.C.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of death", "head": "Robert B", "tail": "Washington"}]


Instruction:
Text: Born in Granville , Manche, France, Jacky Robert began to cook at age 15. After apprenticing at Les Gourmets in Granville, he moved to Paris, where he then worked at Prunier Traktir and at three star Michelin rated Maxim's Paris, rue Royale, working with Wolfgang Puck. Robert staged at Moulin de Mougins, Roger VergA's three star Michelin restaurant and at the Hotel Negresco in Nice, France with Jacques Maximin. Robert worked at Olympia Turm in Munich, Germany, Chez Valentino in Geneva, Switzerland, and Auberge de Riquewhir, Paris. Robert then worked at The Dome of the Four Seasons, Fort Lauderdale, Florida, and Le Cordon Bleu in Dania, Florida, Chillingsworth in Brewster, Cape Cod, Massachusetts, and in 1976, helped open Maison Robert, with restaurant owner and uncle, Lucien Robert, in Boston's Old City Hall.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of birth", "head": "Jacky Robert", "tail": "Granville"}]


Instruction:
Text: Mr. Mark Kaplan graduated cum laude from Bucknell University in 1986 with a major in business management and a minor in international relations. He received his law degree in law from Duke University where he graduated with honors in 1989.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education degree", "head": "Mark Kaplan", "tail": "degree in law"}]


Instruction:
Text: before his 70th wedding anniversary, Karl Malden was inducted into the Wall Doctor of Humane Letters Legends, at St. Sava Church in San Gabriel, California, where Milan Opacich paid tribute to a wonderful man who was a great benefactor of a Serbian Ortodox church.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education degree", "head": "Karl Malden", "tail": "Doctor of Humane Letters"}]


Instruction:
Text: Crispin Davis earned a bachelor's degree from Oriel College at Oxford University. He was knighted by Queen Elizabeth in 2004 for services to the information industry. Davis and his wife, Anne, have three daughters.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education institution", "head": "Crispin Davis", "tail": "Oriel College"}]


Instruction:
Text: Manuel de Araujo de Arajo is a Mozambican politician for the constituency of central Zambezia. He was educated in Mozambique, Zimbabwe, and in the UK at the School of Oriental and African Studies . He is currently studying for a PhD at the University of East Anglia.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education institution", "head": "Manuel de Araujo", "tail": "School of Oriental and African Studies"}]


Instruction:
Text: Henry Segerstrom T. Segerstrom, the managing partner Stanford Graduate School of Business South Coast Plaza and founding chairman of the Orange County Performing Arts Center which bears his family is name, died today. He was 91. Segerstrom was still managing partner right until the end, South Coast Plaza spokeswoman Debra Gunn Downing said. He is going to be sadly missed. It is a huge loss for us.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education institution", "head": "Henry Segerstrom", "tail": "Stanford Graduate School of Business"}]


Instruction:
Text: UK, at the University of Rob Sheffield , with Honors. He completed his internal medicine residency and cardiovascular medicine fellowship at the University of Arizona before completing his interventional cardiology fellowship at Yale University . His interests outside interventional cardiology include treatments for peripheral arterial and venous disease.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education institution", "head": "Rob Sheffield", "tail": "Yale University"}]


Instruction:
Text: Diamond Donald Schmitt Architects is an architectural firm founded in 1975 and located in University of Toronto, main campus Ontario, Canada. The firm currently employs 137 people.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education institution", "head": "Donald Schmitt", "tail": "University of Toronto, main campus"}]


Instruction:
Text: Darryl Stonum graduated early from Dulles High School in January 2008 with plans to get a jump on his graduation and with spring ball at the University of Michigan.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education institution", "head": "Darryl Stonum", "tail": "Dulles High School"}]


Instruction:
Text: Daryl Anderson was born in Seattle, Washington, the son Bachelor of Fine Arts Shirley (nee Gallagher) and Donald Anderson. He began acting in high school and at age 19 joined A Contemporary Theatre in Seattle as box office manager. He started acting professionally in 1972. He received a ((NAM: BFA)) from the University of Washington School of Drama.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education degree", "head": "Daryl Anderson", "tail": "Bachelor of Fine Arts"}]


Instruction:
Text: George A Drew received ((NAM: Honorary degree s)) from Several Universities including
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education degree", "head": "George A", "tail": "Honorary degree"}]


Instruction:
Text: Antonio Abati (late 16th century-1667) was an Italian poet. We know that he was born in Gubbio , but the rest of his youthful biography is very vague.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of birth", "head": "Antonio Abati", "tail": "Gubbio"}]


Instruction:
Text: Born in Champion, Michigan, Donovan received an ((NAM: LL.B.)) Dennis F rom the University Bachelor of Laws Michigan Law School in 1913. He was in private practice in Duluth, Minnesota from 1914 to 1945.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education degree", "head": "Dennis F", "tail": "Bachelor of Laws"}]


Instruction:
Text: Claude Burton (5 July 1903 Littleton , Colorado -- 17 April 1974 Compton, California) was an American racecar driver.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of birth", "head": "Claude Burton", "tail": "Littleton"}]


Instruction:
Text: David Merrick , original name David Margulois born November 27, 1911, St. Louis, Missouri, U.S.-died April 25, 2000, London , England, prolific American theatrical producer who staged many of the most successful plays in American theatre during the 1960s.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of death", "head": "David Merrick", "tail": "London"}]


Instruction:
Text: Arie Albertus Albert Clement born 9 August 1962, Middelburg is professor of musicology at the Utrecht University College Roosevelt, a small liberal arts college in Middelburg, 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 ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education institution", "head": "Albert Clement", "tail": "Utrecht University"}]


Instruction:
Text: Wies aw Je D rusik (born November 27, 1945 in Dabrowa Gornicza) is a Polish politician. He was elected to Sejm on September 25, 2005 getting 9761 votes in 32 Sosnowiec district, candidating from Democratic Left Alliance list.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of birth", "head": "Wies", "tail": "D"}]


Instruction:
Text: Barry Long  (born January 3, 1949) is a retired Canadian professional ice hockey player who played 280 games in the National Hockey League and 387 games in the World Hockey Association. Born in Red Deer , Alberta, he played for the Los Angeles Kings, Edmonton Oilers, Winnipeg Jets, and Detroit Red Wings.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of birth", "head": "Barry Long ", "tail": "Red Deer"}]


Instruction:
Text: Jurgen Zimmerling * February 17, 1952 in Oberhausen , Dagger October 8, 2005 was a German politician and a Member of the European Parliament during its 5th and 6th terms. He was a member of the Christian Democratic Union of Germany, which is affiliated with the Group of the European People is Party Christian Democrats and European Democrats in the European Parliament.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of birth", "head": "Jurgen Zimmerling", "tail": "Oberhausen"}]


Instruction:
Text: Arthur Butterworth attended the Royal Manchester College of Music (now the Royal Northern College of Music), where he studied composition with Richard Hall and also learned the trumpet and conducting. He served as a trumpeter in the Scottish National Orchestra (now the Royal Scottish National Orchestra) from 1949--55 and in the HallA from 1955--62; he also played as a freelance until 1963. In that year he began to teach at the Huddersfield School of Music, an activity which he combined with composing and conducting. He was made an MBE in 1995. He is unrelated to the composer George Butterworth (1885--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 ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education institution", "head": "Arthur Butterworth", "tail": "Royal Manchester College of Music"}]


Instruction:
Text: Stephen Grey (b. 1968 in Rotterdam , Netherlands) is an award-winning British investigative journalist and author best known for revealing details of the CIA's program of 'extraordinary rendition. ' He has also reported extensively from the conflicts in Iraq and Afghanistan.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of birth", "head": "Stephen Grey", "tail": "Rotterdam"}]


Instruction:
Text: John William Ditter Jr. born October 19, 1921 is a Senior United States District Judge Bachelor of Arts the United States District Court for the Eastern District of Pennsylvania.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education degree", "head": "John William Ditter", "tail": "Bachelor of Arts"}]


Instruction:
Text: Road have shifted from aff Lu Diping ent local residents to visitors from around the country. Close by is the Central Market, a century-old outdoor market today specialising in electronic components and digital media. Further west is the Nanjing Road pedestrian mall. Located there are most of Shanghai is oldest and largest department stores, as well as a variety of domestic retail outlets, and some traditional eateries with a long history.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of death", "head": "Lu Diping", "tail": "Nanjing"}]


Instruction:
Text: Ina Ranalli nee Pilkington, Francis , 96, Bachelor of Music Broomall, PA., passed away on July 3, 2017. Born in Gaspe Quebec, Canada in 1920, she was the beloved daughter of the late Arthur and Victoria Francis nee Hammond and loving wife of the late Adam Ranalli. Ina spent her life taking care of her family and enjoyed spending time with her grandchildren and great-grandchildren. Ina was the loving mother of Marisa Ranalli and the late Francesca Morrow ...
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education degree", "head": "Pilkington, Francis", "tail": "Bachelor of Music"}]


Instruction:
Text: of influence : 1. Willie Thomas Haughey was the Scottish Labour Party is biggest donor, having donated over a million pounds. Haughey now faces allegations of cash for favours over his links to Steven Purcell, who was leader of Glasgow city council and who has confessed to using cocaine. Haughey is alleged to have benefited financially from decisions made by Purcell. Purcell chaired a meeting which agreed to give Haughey nearly Pound1m of public money.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of birth", "head": "Thomas Haughey", "tail": "Glasgow"}]


Instruction:
Text: not do it justice. Photo: Florence Reece at her 85th birthday celebration. Highlander Research and Education Center, Knoxville , Tenn.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of death", "head": "Florence Reece", "tail": "Knoxville"}]


Instruction:
Text: Born in Scranton, Pennsylvania, Richard Paul Conaboy was in the United States Army Air Corps Sergeant from 1945 to 1947. He received a ((NAM: B.A.)) from the University Bachelor of Arts Scranton in 1945 and an LL.B. from Catholic University of America School of Law in 1950. He was in private practice in Scranton, Pennsylvania from 1951 to 1962. He was a Deputy state attorney general of State Workman's Insurance Fund, Pennsylvania from 1953 to 1959. He was a Hearing examiner, Pennsylvania Liquor Control Board from 1959 to 1962. He was a judge on the Court of Common Pleas, Lackawanna County, Pennsylvania from 1962 to 1979. He was a President judge from 1978 to 1979. He was a Commission chair, U.S. Sentencing Commission from 1994 to 1998.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education degree", "head": "Richard Paul Conaboy", "tail": "Bachelor of Arts"}]


Instruction:
Text: Adrian Lombard was born in the city of Coventry , Warwickshire, on 9 January 1915. He was the second of three sons of Arthur, a toolmaker, and Louisa. Lombard was taught at the John Gulson Central Advanced School, and later attended evening classes at the Coventry Technical College. After leaving school at the age of 15, he began training in the drawing office of the Rover Company. Lombard was married to Joan Taylor on 18 April 1940 and the couple had three children, one of whom died in infancy. He was awarded a CBE in the Queen's Birthday Honours in June 1967. Lombard died of a brain haemorrhage at the Derbyshire Royal Infirmary in Derby on 13 July 1967, at the age of 52.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of birth", "head": "Adrian Lombard", "tail": "Coventry"}]


Instruction:
Text: Norfolk County, Massachusetts, to Benjamin Nathan Webb 1667-1739 and Susanna Ballentine. He married Ruth Adams in Braintree on November 23, 1731. Webb was the first called minister of the new Congregational Church in the newly incorporated 1727 Town of Uxbridge . The Uxbridge Congregational Church was officially split from the church at Mendon, Massachusetts. Webb was called on January 6, 1731. This church was the first church to be built in the new town of Uxbridge.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of death", "head": "Nathan Webb", "tail": "Uxbridge"}]


Instruction:
Text: Emma Thompson, Sarah Brown and Fiona Millar , cast their vote for UKIP, which amounted to nearly four percent of the vote. For larks? I do not know, but it is not a bad return given the party is real candidate in Holborn and St Pancras, Giles Game, only managed just over one percent of the vote. Camden School for Girls mock election result based on the parties that stood in Holborn and St Pancras
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education institution", "head": "Fiona Millar", "tail": "Camden School for Girls"}]


Instruction:
Text: In 1998, Eric M. Jackson received a ((NAM: B.A.)) in Economics with honors from Stanford University. He served on the board Bachelor of Arts directors of The Stanford Review. Jackson maintains the book publishing industry blog called Conservative Publisher.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education degree", "head": "Eric M. Jackson", "tail": "Bachelor of Arts"}]


Instruction:
Text: Jefferson Lowndes died at Liverpool at the age of 35.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of death", "head": "Jefferson Lowndes", "tail": "Liverpool"}]


Instruction:
Text: ``In appearance Frank Douglas MacKinnon possessed bushy eyebrows, penetrating eyes, a pronounced angular nose, and firm mouth.'' His daughter, son-in-law and young grandchild were lost aboard the Almeda Star, torpedoed in 1941. His son became bursar of Eton College. Following a sudden heart attack, he died in Charing Cross Hospital .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of death", "head": "Frank Douglas MacKinnon", "tail": "Charing Cross Hospital"}]


Instruction:
Text: She was trustee of a number of voluntary organisations, notably the Caldecott Community, and Bessels Leigh schools. vice-president of the National Association of Voluntary Hostels from 1978 and of Barnardo's from 1989. Faithfull supported the National Children's Bureau, of which she was president. In 1993 she founded the Lucy Faithfull Foundation, which works as a child protection agency helping sexually abused children and their families. She died unmarried in London in 1996.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of death", "head": "Lucy Faithfull", "tail": "London"}]


Instruction:
Text: George Biddlecombe , born at Portsea , was the son of Thomas Biddlecombe of Sheerness Dockyard, who died on 12 September 1844. He was educated at a school kept by Dr. Neave at Portsea, and joined the ship Ocean of Whitby as a midshipman in 1823.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of birth", "head": "George Biddlecombe", "tail": "Portsea"}]


Instruction:
Text: Sheepshead Bay, Brooklyn, State University of New York Maritime College York, Leroy J. Alexanderson graduated from Brooklyn is James Madison High School in 1928. He attended the SUNY Maritime College at Fort Schuyler, then known as the New York Merchant Marine Academy. He graduated in 1930, commissioned in both the Naval Reserve and the Maritime Service.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education institution", "head": "Leroy J. Alexanderson", "tail": "State University of New York Maritime College"}]


Instruction:
Text: Michael Johnson received a ((NAM: B.S.)) from the University Bachelor of Science Wisconsin-- Madison and a PhD and MBA from University of 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 ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education degree", "head": "Michael Johnson", "tail": "Bachelor of Science"}]


Instruction:
Text: Dawson Burns died at Battersea on 22 August 1909, and was buried at Paddington.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of death", "head": "Dawson Burns", "tail": "Battersea"}]


Instruction:
Text: A. J. Wynder attended Fairfield University where he was selected a Second Team All-MAAC in 1987 and helped lead the Stags to back-to-back MAAC Championships and NCAA Tournament appearances in 1986 and 1987.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education institution", "head": "A. J. Wynder", "tail": "Fairfield University"}]


Instruction:
Text: Born in St. Paul, Minnesota, Zita Leeson Weinshienk received a ((NAM: B.A.)) from the University Bachelor of Arts Arizona in 1955 and a J.D. from Harvard Law School in 1958. She was a Probation counselor, legal advisor and referee to the Denver Juvenile Court from 1959--64, a judge on the Denver Municipal Court in Denver, Colorado from 1964--65, on the Denver County Court from 1965--71, and on the Colorado District Court, Second Judicial District from 1972-79.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education degree", "head": "Zita Leeson Weinshienk", "tail": "Bachelor of Arts"}]


Instruction:
Text: Buenaventura Sitjar born 9 December 1739 in Porreres, Majorca - died 3 September 1808 at San Antonio, California was a Franciscan missionary who served in California until his death. He became a monk in April, 1758, joining the College of San Fernando de Mexico. In 1770 he received orders to go to California, and reached San Diego on 21 May 1771.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of death", "head": "Buenaventura Sitjar", "tail": "California"}]


Instruction:
Text: Iulica Ruican (born 29 August 1971 in Cujmir , MehedinCi) is a Romanian rower who won two medals at the 1992 Summer Olympics in Barcelona.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of birth", "head": "Iulica Ruican", "tail": "Cujmir"}]


Instruction:
Text: Chandrachud was educated at Nutan Marathi Vidyalaya high school, Elphinstone College and the ILS Law College in Pune. Justice Y. V. Chandrachud V. Chandrachud died on 14 July 2008 shortly after he was admitted to the Bombay Hospital. He is survived by his wife, Prabha, his son, the sitting Judge of the Bombay High Court Dhananjaya Y. Chandrachud http://bombayhighcourt.nic.in/site/judge/dyc1.html, and his daughter, Nirmala.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education institution", "head": "Y. V. Chandrachud", "tail": "Nutan Marathi Vidyalaya"}]


Instruction:
Text: Beverly Daniel Tatum received her B.A. in psychology from Wesleyan University and her Ph.D. in clinical psychology from the University Doctor of Humane Letters Michigan. She also received an M.A. religious studies from Hartford Seminary. Tatum received an ((NAM: L.H.D.)) from Bates College in 2000.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education degree", "head": "Beverly Daniel Tatum", "tail": "Doctor of Humane Letters"}]


Instruction:
Text: WALLACK, James William Wallack WILLIAM 1791?-1864, actor, second son of William Wallack d. 6 March 1850, at Clarendon Square, London , aged 90, a member of Philip Astley is company, and of his wife, Elizabeth Field Granger, also an actress, was born at Hercules Buildings, Lambeth, most probably in 1791 other accounts have it that he was born on 17 or 20 Aug. 1794. His youngest sister, Elizabeth, was mother of Mrs. Alfred Wigan see Wigan, Alfred.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of birth", "head": "James William Wallack", "tail": "London"}]


Instruction:
Text: Born in Bangalore , India 26 March 1929, India, David Lake received a Jesuit education at St. Xavier's School in Calcutta (1940--44). He was originally a citizen of the United Kingdom, where he studied at Trinity College, Cambridge, receiving his Bachelor of Arts in 1952, and his Master of Arts in 1956. He went on to study at University College of North Wales, where he was awarded a diploma in linguistics in 1965, and studied at the University of Queensland (Ph.D., 1974). He became a naturalized Australian citizen in 1975.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of birth", "head": "David Lake", "tail": "Bangalore"}]


Instruction:
Text: Larry Keith Lawrence March 4, 1931 - July 17, 2010 was an American actor who was a cast member on the ABC soap opera All My Children and was the first American to play the role Bachelor of Laws Henry Higgins in the Broadway production of My Fair Lady.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education degree", "head": "Keith Lawrence", "tail": "Bachelor of Laws"}]


Instruction:
Text: Jan. 1741, s. of William James Simmons  alias Simmonds, barber, of Canterbury. educ. The King is sch. Canterbury until 1755. unm.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education institution", "head": "James Simmons ", "tail": "The King"}]


Instruction:
Text: Scott Rauland received his ((NAM: B.A.)) in European history from the University Bachelor of Arts Chicago in 1981 and his M.A. in Russian language and literature from Ohio State University in 1986. From 1982 through 1984 he studied at the University of Mainz, 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 ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education degree", "head": "Scott Rauland", "tail": "Bachelor of Arts"}]


Instruction:
Text: Charles Garry continued to practice law after the Jonestown incident, his clientele changed and his chance for further national acclaim had passed. His post-Jonestown press conferences of November/December 1978 served as his final public acts. Garry served as President of California Attorneys for Criminal Justice in 1979. Garry died of a stroke in August 1991, at the age of 82 in Berkeley, Calif. California.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of death", "head": "Charles Garry", "tail": "Berkeley, Calif."}]


Instruction:
Text: Kenn Burke , a ballet dancer, was born in Fife , Scotland. He completed his training at the Royal Ballet School in England in 1975. He subsequently returned to Scotland where he performed for the Scottish Ballet in leading roles in Elliot Goldenthal's Othello, Prokofiev's Romeo and Juliet, and Carl Maria von Weber's Le Spectre de la Rose. In 1988, he joined the Hong Kong Ballet as principal dancer.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of birth", "head": "Kenn Burke", "tail": "Fife"}]


Instruction:
Text: Born in Devon , Neil Doncaster graduated from Bristol University in 1992, qualified as a solicitor and worked for four years for Burges Salmon, solicitors. In 2008, he obtained an MBA from the University of East Anglia.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of birth", "head": "Neil Doncaster", "tail": "Devon"}]


Instruction:
Text: Moore received a B.S. in chemistry Edward F rom Virginia Polytechnic Institute and State University Polytechnic Institute in Blacksburg, VA in 1947 and a Ph.D. in Mathematics from Brown University in Providence, RI in June 1950. He worked at UIUC from 1950 to 1952 and was a visiting lecturer at MIT and Harvard simultaneously in 1952 and 1953. Then he worked at Bell Labs for about 10 years. After that, he was a professor at the University of Wisconsin-- Madison from 1966 until he retired in 1985.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education institution", "head": "Edward F", "tail": "Virginia Polytechnic Institute and State University"}]


Instruction:
Text: Catherine Albert Houthuesen 1905-83 was in charge of the Art Department at St Gabriel is from 1939 to 1967, and her contribution to the quality of education offered at the Royal College of Art was invaluable. Her sensitive awareness of other people brought out their best qualities; former students and colleagues treasure memories of her, and the way her friendship enriched their lives.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education institution", "head": "Albert Houthuesen", "tail": "Royal College of Art"}]


Instruction:
Text: Born in Virginia, Illinois, Howard McKibben received a ((NAM: B.S.)) from Bradley University in 1962, an M.P.A. from the University Bachelor of Science Pittsburgh in 1964, and a J.D. from the University of Michigan Law School in 1967. He was in private practice in Minden, Nevada from 1967 to 1971. He was a Deputy district attorney of Douglas County, Nevada from 1969 to 1971. He was a District attorney of Douglas County, Nevada from 1971 to 1977. He was a judge on the Ninth Judicial District Court of the State of Nevada from 1977 to 1984.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education degree", "head": "Howard McKibben", "tail": "Bachelor of Science"}]


Instruction:
Text: Born in Paris , G ranel attended the lycAe Louis-le-Grand and the courses of Michel Alexandre, Jean Hyppolite and, later, of Louis Althusser and Jean Beaufret. He taught in Bordeaux, Toulouse, and Aix, before being appointed professor of philosophy at the UniversitA de Toulouse-Le-Mirail, a position he held from 1972 until his death.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of birth", "head": "G", "tail": "Paris"}]


Instruction:
Text: Born in Yankton, South Dakota, Andrew Wendell Bogue received a ((NAM: B.S.)) from South Dakota State College in 1941 and was in the United States Army, Signal Corps during World War II, from 1943 to 1946. He received an LL.B. from the University Bachelor of Science South Dakota School of Law in 1947, and entered private practice in Parker, South Dakota later that year. He was returned to the military as a Lieutenant in the JAG Corps from 1950 to 1952, and was thereafter a state's attorney of Turner County, South Dakota from 1952 to 1954, resuming his private practice in Parker from 1954 to 1957, and then in Canton, South Dakota until 1967. He was a judge on the Second Judicial District, Sioux Falls, South Dakota from 1967 to 1970.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education degree", "head": "Andrew Wendell Bogue", "tail": "Bachelor of Science"}]


Instruction:
Text: Dr. Philip Franklin Szanto was a gifted teacher who combined clinical and academic skills in a 30-year career as associate pr Bachelor of Science essor of pathology at Chicago Medical School at Rosalind Franklin University of Medicine and Science in North 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 ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "education degree", "head": "Philip Franklin", "tail": "Bachelor of Science"}]


Instruction:
Text: Zhang Lin (born 1983-03-06 in Fuxin, China Liaoning) is a male Chinese rower, who competed for Team China at the 2008 Summer Olympics.
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['place of death', 'place of birth', 'education degree', 'education institution']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "place of birth", "head": "Zhang Lin", "tail": "Fuxin, China"}]


Instruction:
Text: Recognition of proper nouns in Japanese text has been studied as a part of the more general problem of morphological analysis in Japanese text processing -LRB- -LSB- 1 -RSB- -LSB- 2 -RSB- -RRB- .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "part of", "head": "Recognition of proper nouns", "tail": "morphological analysis"}, {"relation": "part of", "head": "proper nouns", "tail": "Japanese text"}, {"relation": "used for", "head": "morphological analysis", "tail": "Japanese text processing"}]


Instruction:
Text: It has also been studied in the framework of Japanese information extraction -LRB- -LSB- 3 -RSB- -RRB- in recent 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 ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "Japanese information extraction", "tail": "It"}]


Instruction:
Text: Our approach to the Multi-lingual Evaluation Task -LRB- MET -RRB- for Japanese text is to consider the given task as a morphological analysis problem in Japanese .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "approach", "tail": "Multi-lingual Evaluation Task -LRB- MET -RRB-"}, {"relation": "used for", "head": "Multi-lingual Evaluation Task -LRB- MET -RRB-", "tail": "Japanese text"}, {"relation": "used for", "head": "morphological analysis problem", "tail": "task"}, {"relation": "used for", "head": "Japanese", "tail": "morphological analysis problem"}]


Instruction:
Text: Our morphological analyzer has done all the necessary work for the recognition and classification of proper names , numerical and temporal expressions , i.e. Named Entity -LRB- NE -RRB- items in the Japanese text .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "morphological analyzer", "tail": "recognition and classification of proper names , numerical and temporal expressions"}, {"relation": "hyponym of", "head": "Named Entity -LRB- NE -RRB- items", "tail": "proper names , numerical and temporal expressions"}, {"relation": "part of", "head": "Named Entity -LRB- NE -RRB- items", "tail": "Japanese text"}]


Instruction:
Text: Amorph recognizes NE items in two stages : dictionary lookup and rule application .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "Amorph", "tail": "NE items"}, {"relation": "part of", "head": "dictionary lookup", "tail": "Amorph"}, {"relation": "conjunction", "head": "dictionary lookup", "tail": "rule application"}, {"relation": "part of", "head": "rule application", "tail": "Amorph"}]


Instruction:
Text: First , it uses several kinds of dictionaries to segment and tag Japanese character strings .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "dictionaries", "tail": "it"}, {"relation": "used for", "head": "dictionaries", "tail": "Japanese character strings"}]


Instruction:
Text: Second , based on the information resulting from the dictionary lookup stage , a set of rules is applied to the segmented strings in order to identify NE items .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "dictionary lookup stage", "tail": "rules"}, {"relation": "used for", "head": "rules", "tail": "NE items"}]


Instruction:
Text: We propose to incorporate a priori geometric constraints in a 3 -- D stereo reconstruction scheme to cope with the many cases where image information alone is not sufficient to accurately recover 3 -- D shape .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "part of", "head": "priori geometric constraints", "tail": "3 -- D stereo reconstruction scheme"}, {"relation": "used for", "head": "image information", "tail": "3 -- D shape"}]


Instruction:
Text: Our approach is based on the iterative deformation of a 3 -- D surface mesh to minimize an objective function .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "iterative deformation of a 3 -- D surface mesh", "tail": "approach"}, {"relation": "used for", "head": "iterative deformation of a 3 -- D surface mesh", "tail": "objective function"}]


Instruction:
Text: We show that combining anisotropic meshing with a non-quadratic approach to regularization enables us to obtain satisfactory reconstruction results using triangulations with few vertices .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "conjunction", "head": "anisotropic meshing", "tail": "non-quadratic approach"}, {"relation": "used for", "head": "anisotropic meshing", "tail": "reconstruction"}, {"relation": "used for", "head": "non-quadratic approach", "tail": "regularization"}, {"relation": "used for", "head": "non-quadratic approach", "tail": "reconstruction"}, {"relation": "used for", "head": "triangulations", "tail": "reconstruction"}]


Instruction:
Text: Structural or numerical constraints can then be added locally to the reconstruction process through a constrained optimization scheme .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "Structural or numerical constraints", "tail": "reconstruction process"}, {"relation": "used for", "head": "constrained optimization scheme", "tail": "Structural or numerical constraints"}]


Instruction:
Text: They improve the reconstruction results and enforce their consistency with a priori knowledge about object shape .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "They", "tail": "reconstruction"}, {"relation": "feature of", "head": "object shape", "tail": "priori knowledge"}]


Instruction:
Text: The strong description and modeling properties of differential features make them useful tools that can be efficiently used as constraints for 3 -- D reconstruction .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "them", "tail": "3 -- D reconstruction"}]


Instruction:
Text: It is based on a weakly supervised dependency parser that can model speech syntax without relying on any annotated training corpus .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "weakly supervised dependency parser", "tail": "speech syntax"}]


Instruction:
Text: Labeled data is replaced by a few hand-crafted rules that encode basic syntactic knowledge .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "hand-crafted rules", "tail": "syntactic knowledge"}]


Instruction:
Text: Bayesian inference then samples the rules , disambiguating and combining them to create complex tree structures that maximize a discriminative model 's posterior on a target unlabeled corpus .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "Bayesian inference", "tail": "rules"}, {"relation": "used for", "head": "them", "tail": "complex tree structures"}, {"relation": "used for", "head": "complex tree structures", "tail": "discriminative model 's posterior"}, {"relation": "used for", "head": "unlabeled corpus", "tail": "discriminative model 's posterior"}]


Instruction:
Text: This posterior encodes sparse se-lectional preferences between a head word and its dependents .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "posterior", "tail": "sparse se-lectional preferences"}]


Instruction:
Text: The model is evaluated on English and Czech newspaper texts , and is then validated on French broadcast news transcriptions .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "evaluate for", "head": "English and Czech newspaper texts", "tail": "model"}, {"relation": "evaluate for", "head": "French broadcast news transcriptions", "tail": "model"}]


Instruction:
Text: Listen-Communicate-Show -LRB- LCS -RRB- is a new paradigm for human interaction with data sources .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "Listen-Communicate-Show -LRB- LCS -RRB-", "tail": "human interaction with data sources"}]


Instruction:
Text: We integrate a spoken language understanding system with intelligent mobile agents that mediate between users and information sources .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "part of", "head": "intelligent mobile agents", "tail": "spoken language understanding system"}]


Instruction:
Text: We have built and will demonstrate an application of this approach called LCS-Marine .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "approach", "tail": "LCS-Marine"}]


Instruction:
Text: A domain independent model is proposed for the automated interpretation of nominal compounds in English .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "domain independent model", "tail": "automated interpretation of nominal compounds"}, {"relation": "feature of", "head": "English", "tail": "nominal compounds"}]


Instruction:
Text: This model is meant to account for productive rules of interpretation which are inferred from the morpho-syntactic and semantic characteristics of the nominal constituents .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "model", "tail": "productive rules of interpretation"}, {"relation": "used for", "head": "morpho-syntactic and semantic characteristics", "tail": "productive rules of interpretation"}, {"relation": "feature of", "head": "morpho-syntactic and semantic characteristics", "tail": "nominal constituents"}]


Instruction:
Text: In particular , we make extensive use of Pustejovsky 's principles concerning the predicative information associated with nominals .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "feature of", "head": "nominals", "tail": "predicative information"}]


Instruction:
Text: We argue that it is necessary to draw a line between generalizable semantic principles and domain-specific semantic information .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "compare", "head": "generalizable semantic principles", "tail": "domain-specific semantic information"}]


Instruction:
Text: We explain this distinction and we show how this model may be applied to the interpretation of compounds in real texts , provided that complementary semantic information are retrieved .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "model", "tail": "interpretation of compounds"}]


Instruction:
Text: We present a new method for detecting interest points using histogram information .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "method", "tail": "detecting interest points"}, {"relation": "used for", "head": "histogram information", "tail": "detecting interest points"}]


Instruction:
Text: Unlike existing interest point detectors , which measure pixel-wise differences in image intensity , our detectors incorporate histogram-based representations , and thus can find image regions that present a distinct distribution in the neighborhood .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "evaluate for", "head": "pixel-wise differences in image intensity", "tail": "interest point detectors"}, {"relation": "part of", "head": "histogram-based representations", "tail": "detectors"}]


Instruction:
Text: The proposed detectors are able to capture large-scale structures and distinctive textured patterns , and exhibit strong invariance to rotation , illumination variation , and blur .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "detectors", "tail": "large-scale structures"}, {"relation": "used for", "head": "detectors", "tail": "distinctive textured patterns"}, {"relation": "used for", "head": "detectors", "tail": "rotation"}, {"relation": "used for", "head": "detectors", "tail": "illumination variation"}, {"relation": "used for", "head": "detectors", "tail": "blur"}, {"relation": "conjunction", "head": "large-scale structures", "tail": "distinctive textured patterns"}, {"relation": "conjunction", "head": "rotation", "tail": "illumination variation"}, {"relation": "conjunction", "head": "illumination variation", "tail": "blur"}]


Instruction:
Text: The experimental results show that the proposed histogram-based interest point detectors perform particularly well for the tasks of matching textured scenes under blur and illumination changes , in terms of repeatability and distinctiveness .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "histogram-based interest point detectors", "tail": "matching textured scenes"}, {"relation": "evaluate for", "head": "repeatability", "tail": "histogram-based interest point detectors"}, {"relation": "conjunction", "head": "repeatability", "tail": "distinctiveness"}, {"relation": "evaluate for", "head": "distinctiveness", "tail": "histogram-based interest point detectors"}]


Instruction:
Text: An extension of our method to space-time interest point detection for action classification is also presented .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "method", "tail": "space-time interest point detection"}, {"relation": "used for", "head": "space-time interest point detection", "tail": "action classification"}]


Instruction:
Text: We have implemented a restricted domain parser called Plume .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "hyponym of", "head": "Plume", "tail": "restricted domain parser"}]


Instruction:
Text: Building on previous work at Carnegie-Mellon University e.g. -LSB- 4 , 5 , 8 -RSB- , Plume 's approach to parsing is based on semantic caseframe instantiation .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "Plume 's approach", "tail": "parsing"}, {"relation": "used for", "head": "semantic caseframe instantiation", "tail": "Plume 's approach"}]


Instruction:
Text: This has the advantages of efficiency on grammatical input , and robustness in the face of ungrammatical input .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "feature of", "head": "ungrammatical input", "tail": "robustness"}]


Instruction:
Text: While Plume is well adapted to simple declarative and imperative utterances , it handles passives , relative clauses and interrogatives in an ad hoc manner leading to patchy syntactic coverage .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "Plume", "tail": "declarative and imperative utterances"}, {"relation": "used for", "head": "it", "tail": "passives"}, {"relation": "used for", "head": "it", "tail": "relative clauses"}, {"relation": "used for", "head": "it", "tail": "interrogatives"}, {"relation": "conjunction", "head": "passives", "tail": "relative clauses"}, {"relation": "conjunction", "head": "relative clauses", "tail": "interrogatives"}]


Instruction:
Text: This paper outlines Plume as it currently exists and describes our detailed design for extending Plume to handle passives , relative clauses , and interrogatives in a general manner .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "Plume", "tail": "passives"}, {"relation": "used for", "head": "Plume", "tail": "relative clauses"}, {"relation": "used for", "head": "Plume", "tail": "interrogatives"}, {"relation": "conjunction", "head": "passives", "tail": "relative clauses"}, {"relation": "conjunction", "head": "relative clauses", "tail": "interrogatives"}]


Instruction:
Text: In this paper , we present an unlexicalized parser for German which employs smoothing and suffix analysis to achieve a labelled bracket F-score of 76.2 , higher than previously reported results on the NEGRA corpus .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "unlexicalized parser", "tail": "German"}, {"relation": "used for", "head": "smoothing", "tail": "unlexicalized parser"}, {"relation": "conjunction", "head": "smoothing", "tail": "suffix analysis"}, {"relation": "used for", "head": "suffix analysis", "tail": "unlexicalized parser"}, {"relation": "evaluate for", "head": "labelled bracket F-score", "tail": "unlexicalized parser"}, {"relation": "evaluate for", "head": "NEGRA corpus", "tail": "unlexicalized parser"}]


Instruction:
Text: In addition to the high accuracy of the model , the use of smoothing in an unlexicalized parser allows us to better examine the interplay between smoothing and parsing results .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "evaluate for", "head": "accuracy", "tail": "model"}, {"relation": "used for", "head": "smoothing", "tail": "unlexicalized parser"}]


Instruction:
Text: This paper presents an unsupervised learning approach to disambiguate various relations between named entities by use of various lexical and syntactic features from the contexts .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "unsupervised learning approach", "tail": "relations between named entities"}, {"relation": "used for", "head": "lexical and syntactic features", "tail": "unsupervised learning approach"}]


Instruction:
Text: It works by calculating eigenvectors of an adjacency graph 's Laplacian to recover a submanifold of data from a high dimensionality space and then performing cluster number estimation on the eigenvectors .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "It", "tail": "submanifold"}, {"relation": "used for", "head": "eigenvectors", "tail": "It"}, {"relation": "feature of", "head": "adjacency graph 's Laplacian", "tail": "eigenvectors"}, {"relation": "used for", "head": "high dimensionality space", "tail": "submanifold"}, {"relation": "used for", "head": "cluster number estimation", "tail": "It"}, {"relation": "used for", "head": "cluster number estimation", "tail": "eigenvectors"}]


Instruction:
Text: Experiment results on ACE corpora show that this spectral clustering based approach outperforms the other clustering methods .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "evaluate for", "head": "ACE corpora", "tail": "spectral clustering based approach"}, {"relation": "evaluate for", "head": "ACE corpora", "tail": "clustering methods"}, {"relation": "compare", "head": "spectral clustering based approach", "tail": "clustering methods"}]


Instruction:
Text: This paper proposes a generic mathematical formalism for the combination of various structures : strings , trees , dags , graphs , and products of 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 ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "hyponym of", "head": "strings", "tail": "structures"}, {"relation": "conjunction", "head": "strings", "tail": "trees"}, {"relation": "hyponym of", "head": "trees", "tail": "structures"}, {"relation": "conjunction", "head": "trees", "tail": "dags"}, {"relation": "hyponym of", "head": "dags", "tail": "structures"}, {"relation": "conjunction", "head": "dags", "tail": "graphs"}, {"relation": "hyponym of", "head": "graphs", "tail": "structures"}]


Instruction:
Text: This formalism is both elementary and powerful enough to strongly simulate many grammar formalisms , such as rewriting systems , dependency grammars , TAG , HPSG and LFG .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "formalism", "tail": "grammar formalisms"}, {"relation": "hyponym of", "head": "rewriting systems", "tail": "grammar formalisms"}, {"relation": "conjunction", "head": "rewriting systems", "tail": "dependency grammars"}, {"relation": "hyponym of", "head": "dependency grammars", "tail": "grammar formalisms"}, {"relation": "conjunction", "head": "dependency grammars", "tail": "TAG"}, {"relation": "hyponym of", "head": "TAG", "tail": "grammar formalisms"}, {"relation": "conjunction", "head": "TAG", "tail": "HPSG"}, {"relation": "hyponym of", "head": "HPSG", "tail": "grammar formalisms"}, {"relation": "conjunction", "head": "HPSG", "tail": "LFG"}, {"relation": "hyponym of", "head": "LFG", "tail": "grammar formalisms"}]


Instruction:
Text: A mixed-signal paradigm is presented for high-resolution parallel inner-product computation in very high dimensions , suitable for efficient implementation of kernels in image processing .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "mixed-signal paradigm", "tail": "high-resolution parallel inner-product computation"}, {"relation": "used for", "head": "mixed-signal paradigm", "tail": "kernels"}, {"relation": "used for", "head": "kernels", "tail": "image processing"}]


Instruction:
Text: At the core of the externally digital architecture is a high-density , low-power analog array performing binary-binary partial matrix-vector multiplication .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "part of", "head": "high-density , low-power analog array", "tail": "externally digital architecture"}, {"relation": "used for", "head": "binary-binary partial matrix-vector multiplication", "tail": "high-density , low-power analog array"}]


Instruction:
Text: Full digital resolution is maintained even with low-resolution analog-to-digital conversion , owing to random statistics in the analog summation of binary products .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "part of", "head": "random statistics", "tail": "analog summation of binary products"}]


Instruction:
Text: A random modulation scheme produces near-Bernoulli statistics even for highly correlated inputs .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "random modulation scheme", "tail": "near-Bernoulli statistics"}, {"relation": "used for", "head": "highly correlated inputs", "tail": "random modulation scheme"}]


Instruction:
Text: The approach is validated with real image data , and with experimental results from a CID/DRAM analog array prototype in 0.5 cents m CMOS .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "evaluate for", "head": "real image data", "tail": "approach"}]


Instruction:
Text: In this paper we specialize the projective unifocal , bifo-cal , and trifocal tensors to the affine case , and show how the tensors obtained relate to the registered tensors encountered in previous work .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "projective unifocal , bifo-cal , and trifocal tensors", "tail": "affine case"}]


Instruction:
Text: Finally , we show how the estimation of the tensors from point correspondences is achieved through factorization , and discuss the estimation from line correspondences .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "point correspondences", "tail": "estimation of the tensors"}, {"relation": "used for", "head": "factorization", "tail": "tensors"}, {"relation": "used for", "head": "line correspondences", "tail": "estimation"}]


Instruction:
Text: We propose a corpus-based method -LRB- Biber ,1993 ; Nagao ,1993 ; Smadja ,1993 -RRB- which generates Noun Classifier Associations -LRB- NCA -RRB- to overcome the problems in classifier assignment and semantic construction of noun phrase .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "corpus-based method", "tail": "Noun Classifier Associations -LRB- NCA -RRB-"}, {"relation": "used for", "head": "corpus-based method", "tail": "classifier assignment"}, {"relation": "used for", "head": "corpus-based method", "tail": "semantic construction of noun phrase"}, {"relation": "used for", "head": "Noun Classifier Associations -LRB- NCA -RRB-", "tail": "classifier assignment"}, {"relation": "used for", "head": "Noun Classifier Associations -LRB- NCA -RRB-", "tail": "semantic construction of noun phrase"}, {"relation": "conjunction", "head": "classifier assignment", "tail": "semantic construction of noun phrase"}]


Instruction:
Text: The NCA is created statistically from a large corpus and recomposed under concept hierarchy constraints and frequency of occurrences .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "concept hierarchy constraints", "tail": "NCA"}, {"relation": "used for", "head": "frequency of occurrences", "tail": "NCA"}]


Instruction:
Text: The perception of transparent objects from images is known to be a very hard problem in vision .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "images", "tail": "perception of transparent objects"}]


Instruction:
Text: We show how features that are imaged through a transparent object behave differently from those that are rigidly attached to the scene .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "compare", "head": "those", "tail": "features"}]


Instruction:
Text: We present a novel model-based approach to recover the shapes and the poses of transparent objects from known motion .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "model-based approach", "tail": "shapes and the poses of transparent objects"}, {"relation": "used for", "head": "known motion", "tail": "shapes and the poses of transparent objects"}]


Instruction:
Text: The objects can be complex in that they may be composed of multiple layers with different refractive indices .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "part of", "head": "multiple layers", "tail": "they"}, {"relation": "feature of", "head": "refractive indices", "tail": "multiple layers"}]


Instruction:
Text: We have applied it to real scenes that include transparent objects and recovered the shapes of the objects with high accuracy .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "it", "tail": "real scenes"}, {"relation": "used for", "head": "it", "tail": "shapes of the objects"}, {"relation": "part of", "head": "transparent objects", "tail": "real scenes"}, {"relation": "evaluate for", "head": "accuracy", "tail": "shapes of the objects"}]


Instruction:
Text: We propose a novel probabilistic framework for learning visual models of 3D object categories by combining appearance information and geometric constraints .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "probabilistic framework", "tail": "visual models of 3D object categories"}, {"relation": "used for", "head": "appearance information", "tail": "probabilistic framework"}, {"relation": "conjunction", "head": "appearance information", "tail": "geometric constraints"}, {"relation": "used for", "head": "geometric constraints", "tail": "probabilistic framework"}]


Instruction:
Text: A generative framework is used for learning a model that captures the relative position of parts within each of the discretized viewpoints .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "generative framework", "tail": "model"}]


Instruction:
Text: Contrary to most of the existing mixture of viewpoints models , our model establishes explicit correspondences of parts across different viewpoints of the object class .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "compare", "head": "model", "tail": "mixture of viewpoints models"}]


Instruction:
Text: Given a new image , detection and classification are achieved by determining the position and viewpoint of the model that maximize recognition scores of the candidate objects .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "image", "tail": "detection"}, {"relation": "used for", "head": "image", "tail": "classification"}, {"relation": "conjunction", "head": "detection", "tail": "classification"}, {"relation": "used for", "head": "position", "tail": "detection"}, {"relation": "used for", "head": "position", "tail": "classification"}, {"relation": "conjunction", "head": "position", "tail": "viewpoint"}, {"relation": "used for", "head": "viewpoint", "tail": "detection"}, {"relation": "used for", "head": "viewpoint", "tail": "classification"}]


Instruction:
Text: Our approach is among the first to propose a generative proba-bilistic framework for 3D object categorization .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "generative proba-bilistic framework", "tail": "3D object categorization"}]


Instruction:
Text: We test our algorithm on the detection task and the viewpoint classification task by using '' car '' category from both the Savarese et al. 2007 and PASCAL VOC 2006 datasets .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "algorithm", "tail": "detection task"}, {"relation": "used for", "head": "algorithm", "tail": "viewpoint classification task"}, {"relation": "conjunction", "head": "detection task", "tail": "viewpoint classification task"}, {"relation": "evaluate for", "head": "PASCAL VOC 2006 datasets", "tail": "algorithm"}]


Instruction:
Text: We show promising results in both the detection and viewpoint classification tasks on these two challenging datasets .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "evaluate for", "head": "datasets", "tail": "detection and viewpoint classification tasks"}]


Instruction:
Text: We present an application of ambiguity packing and stochastic disambiguation techniques for Lexical-Functional Grammars -LRB- LFG -RRB- to the domain of sentence condensation .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "used for", "head": "ambiguity packing and stochastic disambiguation techniques", "tail": "Lexical-Functional Grammars -LRB- LFG -RRB-"}, {"relation": "used for", "head": "ambiguity packing and stochastic disambiguation techniques", "tail": "sentence condensation"}]


Instruction:
Text: Our system incorporates a linguistic parser/generator for LFG , a transfer component for parse reduction operating on packed parse forests , and a maximum-entropy model for stochastic output selection .
【Relation Extraction】From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are ['conjunction', 'feature of', 'hyponym of', 'used for', 'part of', 'compare', 'evaluate for']. Output the result in json[{'relation':'', 'head':'', 'tail':''}, ].
Answer:

[{"relation": "part of", "head": "linguistic parser/generator", "tail": "system"}, {"relation": "used for", "head": "linguistic parser/generator", "tail": "LFG"}, {"relation": "conjunction", "head": "linguistic parser/generator", "tail": "transfer component"}, {"relation": "part of", "head": "transfer component", "tail": "system"}, {"relation": "used for", "head": "transfer component", "tail": "parse reduction"}, {"relation": "conjunction", "head": "transfer component", "tail": "maximum-entropy model"}, {"relation": "used for", "head": "packed parse forests", "tail": "parse reduction"}, {"relation": "part of", "head": "maximum-entropy model", "tail": "system"}, {"relation": "used for", "head": "maximum-entropy model", "tail": "stochastic output selection"}]