Nissan

Marketing activities

company product service industry brand country location organization person founder position event action founding accusition merger partnership expansion restructuring divestment sale bankruptcy financial_metric business_concept revenue profit loss investment funding market_share competition market_trend regulation innovation sustainability corporate_social_responsibility award date/time year period

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

{'Schema': ['Person', 'Organization', 'Location', 'Date', 'Product'], 'Text': 'The Eiffel Tower in Paris, designed by Gustave Eiffel, is one of the most famous landmarks in France.', 'Entities': [['Eiffel Tower', 'Location'], ['Paris', 'Location'], ['Gustave Eiffel', 'Person'], ['France', 'Location']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Entity", "Type"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nissan, Marketing activities: Nismois the motorsports division of Nissan, founded in 1984. Nismo cars have participated in theAll Japan Sports Prototype Championship,Super GT,IMSA GT Championship,World Sportscar Championship,FIA World Endurance Championship,British Touring Car Championship,Supercars ChampionshipandBlancpain GT Series. Also, they were featured at theWorld Series by Nissanfrom 1998 to 2004. Nissan sponsored theLos Angeles Opengolf tournament from 1987 to 2007. Beginning in 2015, Nissan became thenaming rightssponsor forNissan Stadium, the home of theTennessee TitansandTennessee State Universityfootball teams inNashville.Nissan also became the official sponsor of theHeisman TrophyandUEFA Champions League.Since 2019, Nissan has been the naming rights sponsor forNissan Arena, the home of theBrisbane Bulletsbasketball team andQueensland Firebirdsnetballteam inBrisbane, Australia.

[
    "Q270195 - Nissan-lez-Enserune: commune in Hérault, France",
    "Q37455347 - Nissan: family name",
    "Q20165 - Nissan Motor Co. Ltd.: Japanese company",
    "Q958359 - Nissan: river in southern Sweden",
    "Q41156912 - Nisan: male given name (ניסן)"
]

[
    "Q1543585 - Nismo: motorsport and performance division of automobile manufacturer Nissan",
    "Q18447137 - Nismozza: human settlement in Italy",
    "Q1426333 - Nismo 400R: car model",
    "Q1993566 - Nismo 270R: None",
    "Q130425697 - Nismo Mi Indijanci: None"
]

[
    "Q270195 - Nissan-lez-Enserune: commune in Hérault, France",
    "Q37455347 - Nissan: family name",
    "Q20165 - Nissan Motor Co. Ltd.: Japanese company",
    "Q958359 - Nissan: river in southern Sweden",
    "Q41156912 - Nisan: male given name (ניסן)"
]

[
    "Q636146 - All Japan Sports Prototype Championship: auto racing series in Japan"
]

[
    "Q116601 - International Stadium Yokohama: multisport stadium in Yokohama, Japan",
    "Q1142992 - Nissan Stadium: home venue of Tennessee Titans and Tennessee State Tigers football team"
]

[
    "Q116601 - International Stadium Yokohama: multisport stadium in Yokohama, Japan",
    "Q1142992 - Nissan Stadium: home venue of Tennessee Titans and Tennessee State Tigers football team"
]

[
    "Q320484 - Tennessee Titans: National Football League Franchise located in Nashville, Tennessee",
    "Q103309445 - Tennessee Titans Opening Day, 09.12.1999, Nashville, TN: print by Hatch Show Print",
    "Q7700220 - Tennessee Titans Cheerleaders: cheerleading squad for the Tennessee Titans of the NFL",
    "Q123987588 - Tennessee Titans at Green Bay Packers (August 28, 2003): None",
    "Q123987595 - Tennessee Titans vs. Oakland Raiders, 8 December 2019: None"
]

[
    "Q299531 - Los Angeles Open: tennis tournament",
    "Q2602686 - Los Angeles Open: golf tournament held in Los Angeles, United States",
    "Q6682287 - Los Angeles Open: golf tournament on the LPGA Tour",
    "Q97062858 - Los Angeles Open Data: None",
    "Q3837329 - 1977 Pacific Southwest Open: men's tennis tournament"
]

[
    "Q2024 - 2007: calendar year",
    "Q907344 - 2007: natural number",
    "Q135047694 - 2007: video game developed by Hit'n'Run Digital Studio",
    "Q53389304 - 2007-2008 one-year-period: period of about one year starting in 2007 and ending in 2008",
    "Q15729072 - 2007 Census of the Philippines: item representing census of Philippines 2007 for source"
]

[
    "Q2925638 - Brisbane Bullets: professional team in Australia's National Basketball League"
]

[
    "Q1035067 - Heisman Trophy: annual award for outstanding college football player",
    "Q67569605 - HEISMAN TROPHY AWARD TO \"DOC\" BLANCHARD (NAID 112633): item in the National Archives and Records Administration's holdings"
]

[
    "Q7270942 - Queensland Firebirds: Australian netball team"
]

[
    "Q1465453 - Renault Sport Series: auto racing series in Europe",
    "Q16041288 - World Series by Nissan: None",
    "Q2425527 - 2004 World Series by Nissan season: sports season",
    "Q577461 - 2002 World Series by Nissan season: sports season",
    "Q2425538 - 2003 World Series by Nissan season: sports season"
]

[
    "Q2089 - 1998: year",
    "Q860019 - 1998: natural number",
    "Q9664 - 1998 Winter Olympics: 18th edition of Winter Olympics, in Nagano, Japan",
    "Q101730 - 1998 FIFA World Cup: 16th FIFA football World Cup finals, held in France",
    "Q53385642 - 1998-1999 one-year-period: period of about one year starting in 1998 and ending in 1999"
]

[
    "Q2014 - 2004: year",
    "Q906911 - 2004: natural number",
    "Q8558 - 2004 Summer Olympics: Games of the XXVIII Olympiad, in Athens, Greece",
    "Q28878253 - 2004 Moroccan census: census in Morocco",
    "Q109719080 - 2004 Indonesian People's Representative Council election: None"
]

[
    "Q1379527 - Super GT: auto racing championship in Japan",
    "Q122341726 - Super GTR Racing: 2018 video game",
    "Q11245130 - SUPER GT+: None",
    "Q18640161 - 2015 Super GT season: None",
    "Q22125925 - 2016 Super GT season: Japanese motorsport championship"
]

[
    "Q945048 - FIA World Endurance Championship: auto racing championship held worldwide",
    "Q21155426 - 2016 FIA World Endurance Championship: 5th season of the FIA World Endurance Championship",
    "Q18348695 - 2015 FIA World Endurance Championship: fourth season of the FIA World Endurance Championship",
    "Q15283923 - 2014 FIA World Endurance Championship: third season of the highest-level international sports car endurance championship",
    "Q39091191 - 2018-2019 FIA World Endurance Championship: 7th season of the World Endurance Championship"
]

[
    "Q1573157 - IMSA GT Championship: auto racing championship in the United States",
    "Q84065 - International Motor Sports Association: North American auto racing sanctioning body"
]

[
    "Q849783 - British Touring Car Championship: auto racing championship in the United Kingdom",
    "Q21705922 - british touring car championship online: website on British motor sports",
    "Q4589271 - 1994 British Touring Car Championship season: 37th season of the British Touring Car Championship",
    "Q739012 - 2008 British Touring Car Championship season: sports season",
    "Q642750 - 2007 British Touring Car Championship season: sports season"
]

[
    "Q1684565 - World Sportscar Championship: defunct auto racing series"
]

[
    "Q1782948 - Tennessee State University: public land-grant university in Nashville, Tennessee, United States",
    "Q6141625 - Tennessee State Tigers and Lady Tigers: intercollegiate sports teams of Tennessee State University",
    "Q99433818 - Tennessee State University Avon Williams Campus Bookstore: bookstore in Nashville, TN, United States of America",
    "Q100993840 - Tennessee State University Avon Williams Campus: campus",
    "Q99475330 - Tennessee State University Bookstore: bookshop in Nashville, TN, United States of America"
]

[
    "Q18756 - UEFA Champions League: European association football tournament",
    "Q96621465 - 2021–22 UEFA Champions League: 67th season of the club football tournament",
    "Q63464555 - UEFA Champions League 1996/97: 1997 association football video game",
    "Q63464470 - UEFA Champions League Season 2001/2002: 2002 football video game",
    "Q63464487 - UEFA Champions League Season 1999/2000: 2000 association football video game"
]

Output: None of the candidates


Nissan

Marketing activities

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

{'Schema': [{'designed': {'domain': 'Person', 'range': 'Structure'}}, {'located_in': {'domain': 'Structure', 'range': 'Location'}}, {'part_of': {'domain': 'City', 'range': 'Country'}}, {'friends_with': {'domain': 'Person', 'range': 'Person'}}, {'lives_at': {'domain': 'Person', 'range': 'Location'}}, {'visits': {'domain': 'Person', 'range': 'Location'}}, {'accompanied_by': {'domain': 'Person', 'range': 'Person'}}, {'shares': {'domain': 'Person', 'range': 'Product'}}], 'Text': 'Gustave Eiffel designed the Eiffel Tower, which is located in Paris, France.', 'Relations': [['Gustave Eiffel', 'designed', 'Eiffel Tower'], ['Eiffel Tower', 'located_in', 'Paris'], ['Paris', 'part_of', 'France']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Subject", "Relation", "Object"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nissan, Marketing activities: Nismois the motorsports division of Nissan, founded in 1984. Nismo cars have participated in theAll Japan Sports Prototype Championship,Super GT,IMSA GT Championship,World Sportscar Championship,FIA World Endurance Championship,British Touring Car Championship,Supercars ChampionshipandBlancpain GT Series. Also, they were featured at theWorld Series by Nissanfrom 1998 to 2004. Nissan sponsored theLos Angeles Opengolf tournament from 1987 to 2007. Beginning in 2015, Nissan became thenaming rightssponsor forNissan Stadium, the home of theTennessee TitansandTennessee State Universityfootball teams inNashville.Nissan also became the official sponsor of theHeisman TrophyandUEFA Champions League.Since 2019, Nissan has been the naming rights sponsor forNissan Arena, the home of theBrisbane Bulletsbasketball team andQueensland Firebirdsnetballteam inBrisbane, Australia.

Nissan

Q270195 - Nissan-lez-Enserune: commune in Hérault, France

Q20165 - Nissan Motor Co. Ltd.: Japanese company

Q958359 - Nissan: river in southern Sweden

Q41156912 - Nisan: male given name (ניסן)

Nissan

Q1543585 - Nismo: motorsport and performance division of automobile manufacturer Nissan

Q18447137 - Nismozza: human settlement in Italy

Nissan

P102 - member of political party: the political party of which a person is or has been a member or otherwise affiliated

P1107 - proportion: to be used as a qualifier, value must be between 0 and 1

P1433 - published in: larger work that a given work was published in, like a journal, a website, a collection, a book or a music album

P478 - volume: volume of a book or music release in a collection/series or a published collection of journal issues in a serial publication

P59 - constellation: the area of the celestial sphere of which the subject is a part (from a scientific standpoint, not an astrological one)

Q958359 - Nissan: river in southern Sweden

Q41156912 - Nisan: male given name (ניסן)

Nissan

P571 - inception: time when an entity begins to exist; for date of official opening use P1619

P800 - notable work: notable scientific, artistic or literary work, or other work of significance among subject's works

P112 - founded by: founder or co-founder of this organization, religion, place or entity

P740 - location of formation: location where a group or organization was formed

Nissan

Q911323 - Nissan Leaf: compact electric hatchback manufactured by Nissan

Q38278176 - Nissan Leaf (first generation): battery electric compact car

Q50398548 - Nissan Leaf Nismo Concept: None

Q50398550 - Nissan Leaf Nismo Concept: None

Nissan

Q131822738 - Nissan Murano (Z53): fourth generation of the Nissan Murano

Q11235694 - NISSAN MURANO CROSSOVER STYLE: None

Nissan

Q1135638 - Nissan X-Trail: automobile model series

Q126424490 - Nissan X-Trail I: automobile model

Nissan

Q26186522 - Nissan Kicks: subcompact crossover SUV

Nissan

Q719974 - Nissan Sentra: car model from Nissan

Nissan

Q1820396 - Nissan Titan: Fullsize pickup truck

Nissan

Q26261942 - Nissan Versa: Subcompact car model from Nissan

Q11509844 - Nissan Latio: Subcompact car model from Nissan

Q11509846 - Nissan Note (E12): Second-generation model of the Nissan Note

Nissan

Q1141759 - Nissan Almera: Automobile By Nissan

Nissan

Q1993801 - Nissan Pulsar: Japanese small car

Q18432709 - Nissan Pulsar (Europe): None

Q1569101 - Nissan Pulsar GTI-R: Hot hatch model from Nissan

Nissan

Nissan

Nissan

Q643635 - Nissan Skyline: car model from Nissan

Q4999061 - Infiniti Q50: Compact executive car

Q3342164 - Nissan Skyline (R34): Tenth-generation model of the Nissan Skyline

Nissan

Q1208796 - Mitsubishi Proudia: luxury car model from Mitsubishi Motors

Q1662408 - Infiniti M: Type of luxury car

Nissan

Q87209760 - Nissan Z: seventh generation of the Nissan Z sports car

Q5930093 - Nissan Z engine: motor vehicle engine

Q7041090 - Nissan ZD engine: motor vehicle engine

Q109928056 - Nissan ZD30: straight-4 naturally aspirated diesel engine manufactured by Nissan

Nissan

Q499109 - Nissan GT-R: sports car model by Nissan

Q18980952 - Nissan GT-R LM Nismo: Sports prototype racing car

Q124250512 - Nissan GT-R variants: Japanese sports car variants

Nissan

Nissan

Q738470 - Nissan Micra: car serie
Q120752349 - Nissan Micra (K12): None
Q11086837 - Nissan Micra C+C: None
Q131339943 - Nissan Micra (K10): None
Q131339964 - Nissan Micra (K11): None

Nissan

Q934829 - Nissan Serena: Minivan
Q7651293 - Suzuki Landy: minivan

Nissan

Nissan

Nissan

Q1419007 - Nissan Pathfinder: SUV model from Nissan

Q452035 - Nissan Terrano: SUV model from Nissan

Q50397886 - Nissan Pathfinder Concept: None

Nissan

Q1465915 - Nissan Navara: Pickup truck model from Nissan

Q105312001 - Nissan Frontier: pickup truck model from Nissan

Q134119910 - Nissan Yuanye Z9: mid-size pickup truck

Q107315603 - Nissan Navara (D23): Third-generation model of the Nissan Navara

Nissan

Q1809938 - Nissan Xterra: SUV model from Nissan

Nissan

Q30896335 - Nissan Rogue Warrior Project: None

Nissan

Nissan

Q377287 - Nissan Maxima: car model
Q1675830 - Nissan Cima: motor vehicle

Nissan

Q1814517 - Nissan Vanette: Commercial vehicle produced by Nissan Motor Co.

Q1172697 - Nissan Largo: Nissan passenger van

Nissan

Q109928179 - Nissan Caravan V: Nissan's light commercial van

Q956356 - Nissan Elgrand: minivan produced by Nissan

Nissan

Q15622878 - Nissan Silvia S14: car model

Q21208911 - Nissan Silvia S13: car model

Q15141237 - Nissan Silvia S15: car model

Q1724595 - Nissan 200SX: automobile model series

Nissan

Nissan

Nissan

Nissan

Nissan

Q15181278 - Nissan ZEOD RC: hybrid electric racing car

Nissan

Q109927063 - Nissan Atlas I: Nissan's light and medium-duty truck

Q109927154 - Nissan Atlas II: Nissan's light and medium-duty truck

Nissan

Q132125644 - Nissan Altima: song by Doechii

Q636791 - Nissan Altima: car model from Nissan

Q135398086 - Nissan Altima: 2024 single by Doechii

Q135398076 - Nissan Altima: vocal track by Doechii

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


Nissan

Research and development

company product service industry brand country location organization person founder position event action founding accusition merger partnership expansion restructuring divestment sale bankruptcy financial_metric business_concept revenue profit loss investment funding market_share competition market_trend regulation innovation sustainability corporate_social_responsibility award date/time year period

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

{'Schema': ['Person', 'Organization', 'Location', 'Date', 'Product'], 'Text': 'The Eiffel Tower in Paris, designed by Gustave Eiffel, is one of the most famous landmarks in France.', 'Entities': [['Eiffel Tower', 'Location'], ['Paris', 'Location'], ['Gustave Eiffel', 'Person'], ['France', 'Location']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Entity", "Type"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nissan, Research and development: Nissan's central researchis inside the Oppama Plant site,Yokosuka, which began its operation in 1961, at the former site ofImperial Japanese Navy's Airborne Squadron base. In 1982, Nissan's technical centers inSuginami, TokyoandTsurumi, Yokohamawere combined into one: Nissan Technical Center (NTC) inAtsugi, Kanagawa, at the foot ofMount Ōyamaof theTanzawa Mountains. At its 30th anniversary, in 2012, NTC employed 9,500 employees in product development, design, production engineering, and purchasing. Nissan Technical Center works closely with its overseas operations: Nissan Technical Center (NTC)/North America,NTC/Mexico, Nissan Design America,  NissanSilicon ValleyOffice,and Nissan Technical Centre Europe (NTCE). In 2007, the company opened Nissan Advanced Technology Center (NATC), near the NTC site. It works in close contact with the central research, the Silicon Valley office, the technical office near the Nissan headquarters in centralYokohama, and the overseas offices inDetroit, Silicon Valley, andMoscow. Nissan's test courses are inTochigi(two courses),YokosukaandHokkaido.

[
    "Q28973233 - Nissan Technical Center North America: technical development and engineering center of Nissan North America in Farmington Hills, Michigan, USA"
]

[
    "Q28973233 - Nissan Technical Center North America: technical development and engineering center of Nissan North America in Farmington Hills, Michigan, USA"
]

[
    "Q28973233 - Nissan Technical Center North America: technical development and engineering center of Nissan North America in Farmington Hills, Michigan, USA"
]

[
    "Q389711 - Atsugi: city in Kanagawa prefecture, Japan"
]

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


Nissan

Research and development

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

{'Schema': [{'designed': {'domain': 'Person', 'range': 'Structure'}}, {'located_in': {'domain': 'Structure', 'range': 'Location'}}, {'part_of': {'domain': 'City', 'range': 'Country'}}, {'friends_with': {'domain': 'Person', 'range': 'Person'}}, {'lives_at': {'domain': 'Person', 'range': 'Location'}}, {'visits': {'domain': 'Person', 'range': 'Location'}}, {'accompanied_by': {'domain': 'Person', 'range': 'Person'}}, {'shares': {'domain': 'Person', 'range': 'Product'}}], 'Text': 'Gustave Eiffel designed the Eiffel Tower, which is located in Paris, France.', 'Relations': [['Gustave Eiffel', 'designed', 'Eiffel Tower'], ['Eiffel Tower', 'located_in', 'Paris'], ['Paris', 'part_of', 'France']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Subject", "Relation", "Object"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nissan, Research and development: Nissan's central researchis inside the Oppama Plant site,Yokosuka, which began its operation in 1961, at the former site ofImperial Japanese Navy's Airborne Squadron base. In 1982, Nissan's technical centers inSuginami, TokyoandTsurumi, Yokohamawere combined into one: Nissan Technical Center (NTC) inAtsugi, Kanagawa, at the foot ofMount Ōyamaof theTanzawa Mountains. At its 30th anniversary, in 2012, NTC employed 9,500 employees in product development, design, production engineering, and purchasing. Nissan Technical Center works closely with its overseas operations: Nissan Technical Center (NTC)/North America,NTC/Mexico, Nissan Design America,  NissanSilicon ValleyOffice,and Nissan Technical Centre Europe (NTCE). In 2007, the company opened Nissan Advanced Technology Center (NATC), near the NTC site. It works in close contact with the central research, the Silicon Valley office, the technical office near the Nissan headquarters in centralYokohama, and the overseas offices inDetroit, Silicon Valley, andMoscow. Nissan's test courses are inTochigi(two courses),YokosukaandHokkaido.

Nissan_Technical_Center

Q28973233 - Nissan Technical Center North America: technical development and engineering center of Nissan North America in Farmington Hills, Michigan, USA

P625 - coordinate location: geocoordinates of the subject. For Earth, please note that only the WGS84 geodetic datum is currently supported

P276 - location: location of the object, structure or event; use P131 to indicate the containing administrative entity, P8138 for statistical entities, or P706 for geographic entities; use P7153 for locations associated with the object

P937 - work location: location where persons or organisations were actively participating in employment, business or other work

P3005 - valid in place: place where a statement is valid

P131 - located in the administrative territorial entity: the item is located on the territory of the following administrative entity. Use P276 for specifying locations that are non-administrative places and for items about events. Use P1382 if the item falls only partially into the administrative entity.

Oppama_Plant

P1766 - place name sign: image of (road) sign with place name on it

P194 - legislative body: legislative body governing this entity; political institution with elected representatives, such as a parliament/legislature or council

P1408 - licensed to broadcast to: place that a radio/TV station is licensed/required to broadcast to

Q200578 - Yokosuka: city in Kanagawa Prefecture, Japan

Q11543535 - Yokosuka: dissolved municipality in Hazu district, Aichi prefecture, Japan (1906-1955)

Q11543547 - Yokosuka: dissolved municipality in Chita district, Aichi prefecture, Japan

Q635990 - Yokosuka MXY-7 Ohka: 1945 attack aircraft

Q17209533 - RV Yokosuka: Japanese research support vessel

Nissan_Technical_Center

Q270195 - Nissan-lez-Enserune: commune in Hérault, France

Q20165 - Nissan Motor Co. Ltd.: Japanese company

Q958359 - Nissan: river in southern Sweden

Q41156912 - Nisan: male given name (ניסן)

Nissan_Technical_Center

P154 - logo image: graphic mark or emblem commonly used by commercial enterprises, organizations and products

P10120 - Australian Trade Mark Number: identifier for a trade mark registered in Australia

P10482 - U.S. trademark serial number: identifier of a trademark serial number issued by the United States Patent and Trademark Office

Q219712 - Imperial Japanese Navy: naval branch of the Empire of Japan

Q105768262 - Imperial Japanese Navy fleet: fleet of Imperial Japanese Navy

Q717305 - Imperial Japanese Navy General Staff: highest organ within the Imperial Japanese Navy

Q66736173 - Imperial Japanese Navy unit: military unit organized within the structure of the Imperial Japanese Navy

Q6006697 - Imperial Japanese Navy Aviation Bureau: None

Nissan_Technical_Center

P729 - service entry: date or point in time on which a piece or class of equipment entered operational service

P1810 - subject named as: name by which a subject is recorded in a database, mentioned as a contributor of a work, or is referred to in a particular context

P5166 - established from medical condition: cell line or xenograft established from an individual person or animal with a specific medical condition

Q699574 - 1961 West German federal election: election

Q53388350 - 1961-1962 one-year-period: period of about one year starting in 1961 and ending in 1962

Q4570256 - 1961: episode of Heroes (S3 E23)

Nissan_Technical_Center

Nissan_Technical_Center

Q389711 - Atsugi: city in Kanagawa prefecture, Japan

Nissan_Technical_Center

P1128 - employees: total number of employees of a company at a given "point in time" (P585). Most recent data would generally have preferred rank; data for previous years normal rank (not deprecated rank). Add data for recent years, don't overwrite

P4908 - season: season of a series (televison, web, podcast)

P2437 - number of seasons: number of seasons a television or radio series has had

Nissan_Technical_Center

Q493093 - Tanzawa Mountains: mountains in Japan

Nissan_Technical_Center

Nissan_Advanced_Technology_Center

Nissan_Technical_Center

Nissan_Technical_Center

Nissan_Technical_Center

Nissan_Technical_Center

Nissan_Technical_Center

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Nissan_Technical_Center

Nissan_Technical_Center

Nissan_Technical_Center

Nissan_Technical_Center

Nissan_Technical_Center

Nissan_Technical_Center

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Nissan_Technical_Center

Nissan_Technical_Center

Nissan_Technical_Center

Nissan_Technical_Center

Nissan_Technical_Center

Nissan_Technical_Center

Nissan_Technical_Center

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Nissan

Nissan Digital Hubs

company product service industry brand country location organization person founder position event action founding accusition merger partnership expansion restructuring divestment sale bankruptcy financial_metric business_concept revenue profit loss investment funding market_share competition market_trend regulation innovation sustainability corporate_social_responsibility award date/time year period

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

{'Schema': ['Person', 'Organization', 'Location', 'Date', 'Product'], 'Text': 'The Eiffel Tower in Paris, designed by Gustave Eiffel, is one of the most famous landmarks in France.', 'Entities': [['Eiffel Tower', 'Location'], ['Paris', 'Location'], ['Gustave Eiffel', 'Person'], ['France', 'Location']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Entity", "Type"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nissan, Nissan Digital Hubs: In mid-2018, Nissan launched its first of many planned software and information technology development centers inThiruvananthapuram, Kerala, India.

[
    "Q270195 - Nissan-lez-Enserune: commune in Hérault, France",
    "Q37455347 - Nissan: family name",
    "Q20165 - Nissan Motor Co. Ltd.: Japanese company",
    "Q958359 - Nissan: river in southern Sweden",
    "Q41156912 - Nisan: male given name (ניסן)"
]

[
    "Q162612 - Thiruvananthapuram district: district of Kerala, India",
    "Q167715 - Thiruvananthapuram: city in southern India; the capital of Kerala",
    "Q1466205 - Thiruvananthapuram International Airport: international airport serving Kerala, India",
    "Q16137289 - Thiruvananthapuram State Assembly constituency: constituency of the Kerala legislative assembly in India",
    "Q7785502 - Thiruvananthapuram Lok Sabha constituency: Lok Sabha Constituency in Kerala"
]

Output: None of the candidates


Nissan

Nissan Digital Hubs

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

{'Schema': [{'designed': {'domain': 'Person', 'range': 'Structure'}}, {'located_in': {'domain': 'Structure', 'range': 'Location'}}, {'part_of': {'domain': 'City', 'range': 'Country'}}, {'friends_with': {'domain': 'Person', 'range': 'Person'}}, {'lives_at': {'domain': 'Person', 'range': 'Location'}}, {'visits': {'domain': 'Person', 'range': 'Location'}}, {'accompanied_by': {'domain': 'Person', 'range': 'Person'}}, {'shares': {'domain': 'Person', 'range': 'Product'}}], 'Text': 'Gustave Eiffel designed the Eiffel Tower, which is located in Paris, France.', 'Relations': [['Gustave Eiffel', 'designed', 'Eiffel Tower'], ['Eiffel Tower', 'located_in', 'Paris'], ['Paris', 'part_of', 'France']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Subject", "Relation", "Object"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nissan, Nissan Digital Hubs: In mid-2018, Nissan launched its first of many planned software and information technology development centers inThiruvananthapuram, Kerala, India.

Nissan

P1056 - product or material produced: material or product, including services, produced or provided by an organization, industry, facility, or process

P176 - manufacturer: (main or final) manufacturer or producer of this product

P2079 - fabrication method: method, process or technique used to grow, cook, weave, build, assemble, manufacture the item

P272 - production company: company that produced this film, audio or performing arts work

P1071 - location of creation: place where the item was conceived or made; where applicable, location of final assembly


Nissan

Manufacturing locations

company product service industry brand country location organization person founder position event action founding accusition merger partnership expansion restructuring divestment sale bankruptcy financial_metric business_concept revenue profit loss investment funding market_share competition market_trend regulation innovation sustainability corporate_social_responsibility award date/time year period

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

{'Schema': ['Person', 'Organization', 'Location', 'Date', 'Product'], 'Text': 'The Eiffel Tower in Paris, designed by Gustave Eiffel, is one of the most famous landmarks in France.', 'Entities': [['Eiffel Tower', 'Location'], ['Paris', 'Location'], ['Gustave Eiffel', 'Person'], ['France', 'Location']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Entity", "Type"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nissan, Manufacturing locations: Data extracted from Nissan's international corporate website.

[
    "Q270195 - Nissan-lez-Enserune: commune in Hérault, France",
    "Q37455347 - Nissan: family name",
    "Q20165 - Nissan Motor Co. Ltd.: Japanese company",
    "Q958359 - Nissan: river in southern Sweden",
    "Q41156912 - Nisan: male given name (ניסן)"
]

[
    "Q270195 - Nissan-lez-Enserune: commune in Hérault, France",
    "Q37455347 - Nissan: family name",
    "Q20165 - Nissan Motor Co. Ltd.: Japanese company",
    "Q958359 - Nissan: river in southern Sweden",
    "Q41156912 - Nisan: male given name (ניסן)"
]