Nissan

Nissan

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: Nissan Motor Co., Ltd.[b],doing business asNissanand formerlyJidosha-Seizo, is a Japanese multinationalautomobile manufacturerheadquartered inYokohama, Kanagawa, Japan. The company sells its vehicles under theNissanandInfinitibrands, and formerly theDatsunbrand, with in-house performance tuning products (including cars) under theNismoandAutechbrands. The company can be traced back to the beginning of the 20th century, with the Nissanzaibatsuor calledNissan Group. Since 1999, Nissan has been part of theRenault–Nissan–Mitsubishi Alliance(Mitsubishi joining in 2016), a partnership between Nissan andMitsubishi Motorsof Japan, withRenaultof France. As of November 2023[update], Renault holds a 15% voting stake in Nissan, while Nissan holds the same stake in Renault.Since October 2016, Nissan held a 34% controlling stake in Mitsubishi Motors.In November 2024, Nissan reduced its stake in Mitsubishi Motors from 34% to 24%. In 2017, Nissan was the sixth largest automaker in the world, afterToyota,Volkswagen Group,Hyundai Motor Group,General MotorsandFord.With a revenue of $78 billion in 2022, Nissan was the ninth largest automobile maker in the world. Nissan planned to merge withHonda Motor Companyin 2026, after an announcement in December 2024.However by February 2025, Nissan announced it would abandon merger plans as the automaker stated that it wanted to become an equal partner to Honda rather than a subsidiary.In November 2024, a Nissan executive was quoted as saying that the company had as little as 12 months left to live, barring any major events.As of 2025[update], Nissan is having major financial issues.

[
    "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)"
]

[
    "Q17 - Japan: island country in East Asia",
    "Q13989 - Honshu: largest island of Japan",
    "Q5287 - Japanese: language spoken in East Asia",
    "Q188712 - Empire of Japan: Empire in the Asia-Pacific region from 1868 to 1947",
    "Q205662 - Tokugawa shogunate: 1603–1868 Japanese military government"
]

[
    "Q6686 - Renault: automotive conglomerate",
    "Q98584518 - Renault: French automobile manufacturer",
    "Q453485 - Jean-Baptiste Regnault: French painter (1754–1829)",
    "Q2636184 - Renault: agricultural machinery division of the car manufacturer, established in 1918",
    "Q25333048 - Renault: family name"
]

[
    "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)"
]

[
    "Q27543 - Datsun: automobile brand of Nissan",
    "Q117080876 - Datsun: general cargo vessel built in 2005",
    "Q122948395 - Datsun: Norwegian musical group",
    "Q1664019 - The Datsuns: hard rock band from Cambridge, New Zealand",
    "Q846552 - Datsun Truck: pickup"
]


Nissan

Nissan

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: Nissan Motor Co., Ltd.[b],doing business asNissanand formerlyJidosha-Seizo, is a Japanese multinationalautomobile manufacturerheadquartered inYokohama, Kanagawa, Japan. The company sells its vehicles under theNissanandInfinitibrands, and formerly theDatsunbrand, with in-house performance tuning products (including cars) under theNismoandAutechbrands. The company can be traced back to the beginning of the 20th century, with the Nissanzaibatsuor calledNissan Group. Since 1999, Nissan has been part of theRenault–Nissan–Mitsubishi Alliance(Mitsubishi joining in 2016), a partnership between Nissan andMitsubishi Motorsof Japan, withRenaultof France. As of November 2023[update], Renault holds a 15% voting stake in Nissan, while Nissan holds the same stake in Renault.Since October 2016, Nissan held a 34% controlling stake in Mitsubishi Motors.In November 2024, Nissan reduced its stake in Mitsubishi Motors from 34% to 24%. In 2017, Nissan was the sixth largest automaker in the world, afterToyota,Volkswagen Group,Hyundai Motor Group,General MotorsandFord.With a revenue of $78 billion in 2022, Nissan was the ninth largest automobile maker in the world. Nissan planned to merge withHonda Motor Companyin 2026, after an announcement in December 2024.However by February 2025, Nissan announced it would abandon merger plans as the automaker stated that it wanted to become an equal partner to Honda rather than a subsidiary.In November 2024, a Nissan executive was quoted as saying that the company had as little as 12 months left to live, barring any major events.As of 2025[update], Nissan is having major financial issues.

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)

Q29714 - Infiniti: Japanese luxury vehicle brand owned by Nissan

Q100952920 - zu infinitive: verb form in German

Q173663 - Red Bull Racing: Formula One racing team based in Milton Keynes, England

Q299531 - Los Angeles Open: tennis tournament

Nissan

Q27543 - Datsun: automobile brand of Nissan

Q117080876 - Datsun: general cargo vessel built in 2005

Q122948395 - Datsun: Norwegian musical group

Q1664019 - The Datsuns: hard rock band from Cambridge, New Zealand

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

P355 - child organization/unit: child organization/unit or subsidiary of a company or organization/unit; generally a fully owned separate corporation. Compare with "business division" (P199). Opposite of parent organization/unit (P749).

P749 - parent organization: parent organization of an organization, opposite of subsidiaries (P355)

Nissan

Q6686 - Renault: automotive conglomerate

Q98584518 - Renault: French automobile manufacturer

Q453485 - Jean-Baptiste Regnault: French painter (1754–1829)

Q2636184 - Renault: agricultural machinery division of the car manufacturer, established in 1918

Nissan

Q784512 - Autechaux-Roide: commune in Doubs, France

Q784505 - Autechaux: commune in Doubs, France

Q247237 - Autechre: English electronic music duo

Q784450 - NISSAN MOTORSPORTS & CUSTOMIZING: company

Q11189744 - AUTECH: Special vehicle by AUTECH

Nissan

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

Q18447137 - Nismozza: human settlement in Italy

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

Q36033 - Mitsubishi Motors: Japanese automotive manufacturer

Q6883104 - Mitsubishi Mizushima FC: Japanese association football club

Q1122373 - Ralliart: motorsports division of Mitsubishi Motors

Q264136 - Badminton Horse Trials: equestrian triathalon

Q183275 - Mitsubishi: group of autonomous, Japanese multinational companies

Nissan

P47 - shares border with: countries or administrative subdivisions, of equal level, that this item borders, either by land or water. A single common point is enough.

P4070 - identifier shared with: qualifier, to be used on external identifier IDs, indicating another Wikidata item is also matched to this ID

P9531 - identifier shared with lexeme: qualifier used to state which other lexemes are also matched to this identifier

P10756 - number of reblogs: number of reblogs, reposts, retweets, crossposts, or on-platform shares received on a social media post

Q110420023 - 15.000.000 parachutes: film by Sebastian Diaz Morales

Q16487287 - 15 mil dibujos: Chilean animated film

Nissan

Nissan

P2139 - total revenue: income gained by an organization during a given time frame. Not to be confused with fiscal revenue

P2770 - source of income: source of income of an organization or person

Nissan

P1545 - series ordinal: position of an item in its parent series (most frequently a 1-based index), generally to be used as a qualifier (different from "rank" defined as a class, and from "ranking" defined as a property for evaluating a quality)

P105 - taxon rank: level in a taxonomic hierarchy

P1352 - ranking: subject's ordinal position as qualitatively evaluated relative to other members of a group

P410 - military or police rank: military or police rank achieved by a person (should usually have a "start time" qualifier), or military or police rank associated with a position

P5242 - ARWU university ID: identifier for a university on Shanghai-Rankings website

Q1838 - terbium: element with the atomic number of 65

Q23649981 - 6: i rating category in the Kijkwijzer system

Q771 - Massachusetts: state of the United States of America

Nissan

Q9584 - Honda: manufacturer of automobiles, motorcycles, and power equipment

Q17049550 - Honda Motor Company v. Oberg: 1994 United States Supreme Court case

Q117947531 - Honda Motor Company Gent: None

Nissan

Q28748062 - 24.02.1967: 2017 studio album by Gigi D'Alessio

Q2815175 - 24.000 baci: 2011 debut studio album by Aylin Prandi

Q15223871 - 24.000 baci: 1961 song performed by Adriano Celentano

Nissan

Q6686 - Renault: automotive conglomerate

Q98584518 - Renault: French automobile manufacturer

Q453485 - Jean-Baptiste Regnault: French painter (1754–1829)

Nissan

Q20165 - Nissan Motor Co. Ltd.: Japanese company

Q1993716 - Nissan Motor Ibérica: Spanish subsidiary company

Q11509889 - Nissan Motor Co. Baseball Club: None

Q9544829 - Nissan Motor Indonesia: Indonesian subsidiary company

Nissan

Q11456087 - FUJI CORPORATION: healthcare organization in Chiryū, Aichi Pref., Japan

Nissan

Q26823 - Abarth: Italian racing car maker

Q4573645 - Abarth 1000 GT Coupé: Alfa Romeo car

Q112220966 - Abarth T140: Concept racingcar

Q26234047 - Abarth 124 Spider: Sports car

Nissan

Q26921 - Alfa Romeo: Italian automotive manufacturer

Q124982078 - Alfa Romeo: Italian car brand

Q622489 - Alfa Romeo Racing: Formula One team

Q61467970 - Alfa Romeo: 1950-1951 period of the Alfa Romeo Formula One team

Q84562554 - Alfa Romeo: 1979-1985 period of the Alfa Romeo Racing Formula One team

Nissan

Q2487297 - Academic Medical Center: university hospital affiliated with the Universiteit van Amsterdam

Q4652575 - AMC Networks: American entertainment company

Q2826927 - Agnes Mary Clerke: Irish astronomer and writer (1842–1907)

Q171764 - Ahmadiyya: Islamic revival movement originating in the Indian subcontinent

Nissan

Q27586 - Ferrari: Italian luxury sports car manufacturer; subsidiary of Ferrari N.V.

Q2254817 - Ferrari: 2003 film by Carlo Carlei

Q55516950 - Ferrari: 2018 single by Bebe Rexha

Nissan

Q35886 - Lamborghini: Italian car manufacturer

Q318094 - Lamborghini Aventador: car model

Q124966860 - Lamborghini: car brand owned by Automobili Lamborghini S.p.A.

Q98579650 - Lamborghini: tractor manufacturer

Nissan

Q35962 - Maserati: Italian luxury car manufacturer

Q124991042 - Maserati: Italian automobile brand

Q1548973 - Maserati: American post-rock band

Q2666342 - Maserati: Formula One constructor team

Nissan

Q749239 - Pagani Automobili: Italian car manufacturer

Q29536 - paganism: polytheistic religious groups

Q132560748 - Pagani: car brand owned by Pagani Automobili S.p.A.

Nissan

Q6686 - Renault: automotive conglomerate

Q98584518 - Renault: French automobile manufacturer

Q453485 - Jean-Baptiste Regnault: French painter (1754–1829)

Nissan

Q219501 - Saab Group: Swedish aerospace and defense company

Q48957 - Saab Automobile: Swedish automotive company (1945-2011)

Q277491 - Saab 340: regional airliner family

Nissan

Q7523322 - Singapore-MIT Alliance for Research and Technology: None

Q22645 - smartphone: handheld mobile telephone/computer

Q620615 - mobile app: software application designed to run on mobile devices

Nissan

Q181642 - Suzuki Motor Corporation: Japanese multinational automotive corporation

Q1533615 - Suzuki: Japanese family name (鈴木)

Q360978 - Suzuki Harunobu: Japanese printmaker

Nissan

Q246 - Volkswagen: German automotive brand; manufacturing subsidiary of Volkswagen Group

Q156578 - Volkswagen Group: German automotive manufacturing conglomerate

Q1766743 - Volkswagen Foundation: private German charitable Foundation

Q1417237 - Volkswagen Bora: Saloon car related to the MK4 Golf.

Q155603 - Volkswagen Arena: football stadium in Wolfsburg, Germany

Nissan

Q2557186 - Zenvo Automotive: car manufacturer from Denmark

Q31837609 - Zenvo TS1: grand touring sports car manufactured by Danish automobile manufacturer Zenvo

Nissan

Nissan

Q482994 - album: grouping of album releases by an artist usually released at the same time with the same title and tracks but in different formats for consumption (digital, CD, LP)

Q23317 - Audi AG: German automotive manufacturing subsidiary of Volkswagen Group

Q7302866 - audio track: track on an audio release

Q128124 - audio engineer: engineer who operates recording, mixing, sound reproduction equipment

Nissan

Q26678 - BMW: German automobile manufacturer, and conglomerate

Q249173 - BMW Motorrad: motorcycle brand of the German company BMW

Q98582386 - Black Myth: Wukong: 2024 video game developed by Game Science

Nissan

Q27597 - Fiat: automotive and aircraft brand manufacturing subsidiary of FCA Italy

Q940689 - Fiat: Italian multinational automotive manufacturing (1899-2014)

Q18042054 - TXLNG: protein-coding gene in the species Homo sapiens

Q324744 - Fiat G.91: attack aircraft family by Fiat

Nissan

Q6742 - Peugeot: French automotive brand of Stellantis

Q1624386 - Peugeot cycling team: French professional cycling team (1901–1986)

Q275623 - Groupe PSA: French automotive manufacturing corporation (1976-2021)

Nissan

Q6746 - Citroën: French automotive manufacturer

Q30024860 - Citroën: entry in Dictionnaire du patrimoine rennais

Nissan

Q27530 - Mercedes-Benz Group: German automotive manufacturer

Q386598 - Daimler Buses: German bus and coach manufacturer

Q1157624 - Daimler Truck: German commercial vehicle manufacturer

Q27539 - Daimler Company: British motor vehicle manufacturer

Nissan

Q36008 - Mercedes-Benz: German automobile brand owned by Mercedes-Benz AG

Q541477 - Daimler-Benz AG: 1926-1998 automotive and engine manufacturer in Germany, later restructured and renamed; best-known products being the Mercedes-Benz cars

Q1427345 - Mercedes-Benz buses: German manufacturer of buses and coaches

Q152349 - MHPArena: football stadium in Stuttgart, Germany

Nissan

Q40966 - Opel: German automobile manufacturer, subsidiary of Stellantis

Nissan

Q40990 - Pontiac: automobile brand of General Motors

Q574912 - Pontiac: city in Illinois, USA

Q847596 - Pontiac: city and county seat of Oakland County, Michigan, United States

Q3397733 - Pontiac: municipality in Quebec, Canada

Q1517934 - Pontiac: regional county municipality in Quebec, Canada

Nissan

Q35896 - Lancia: automobile manufacturing subsidiary of Stellantis

Nissan

Q2254817 - Ferrari: 2003 film by Carlo Carlei

Q55516950 - Ferrari: 2018 single by Bebe Rexha

Nissan

Q183275 - Mitsubishi: group of autonomous, Japanese multinational companies

Q36033 - Mitsubishi Motors: Japanese automotive manufacturer

Q650745 - Mitsubishi Corporation: Japanese trading company

Q1542175 - Mitsubishi UFJ Financial Group: Japanese bank holding and financial services company

Q1190247 - Mitsubishi Fuso Truck and Bus Corporation: Japanese manufacturer of commercial vehicles

Nissan

Q146747 - Vauxhall: area in the London Borough of Lambeth, England

Q86666660 - Vauxhall station: railway station in London; interchange with tube station of the same name

Q59187 - Vauxhall: English car company, subsidiary of Stellantis

Q873637 - Vauxhall: Parliamentary constituency in the United Kingdom, 1950-2024

Q105070490 - Vauxhall: British car brand

Nissan

Q63049904 - Aston Martin: British luxury sports car marque

Q27074 - Aston Martin Lagonda: British luxury sports car manufacturer

Q104620704 - Aston Martin F1 Team: Formula One racing team

Q116107407 - Aston Martin: 2021 single by Shiva featuring Headie One

Nissan

Q30055 - Jaguar Cars: former British car company

Q21170490 - Jaguar: British car marque used by Jaguar Land Rover

Q2732887 - Jaguar: 1928 Type 24 torpedo boat

Q35694 - jaguar: species of big cat native to the Americas

Q11859320 - Jaguar: 1923 Chacal-class destroyer

Nissan

Q219501 - Saab Group: Swedish aerospace and defense company

Q48957 - Saab Automobile: Swedish automotive company (1945-2011)

Q277491 - Saab 340: regional airliner family

Nissan

Q4652575 - AMC Networks: American entertainment company

Q2826927 - Agnes Mary Clerke: Irish astronomer and writer (1842–1907)

Nissan

Q28260 - Lincoln: city in and the county seat of Lancaster County, Nebraska, United States, that is also the state capital

Q32433 - Lincoln: 2012 film directed by Steven Spielberg

Q18519 - Lincoln City F.C.: association football club in Lincoln, England

Nissan

Nissan

Q27401 - Bugatti: former German and French automaker

Q2308012 - Bugatti Automobiles: French high-performance luxury automobile manufacturer

Q4986135 - Bugatti: 2013 single by Ace Hood, Rick Ross, Future

Nissan

Q15678788 - Fiat Chrysler Automobiles: Italian-American multinational automotive manufacturing (2014-2021)

Q3744458 - Stellantis Europe: Italian subsidiary of Franco-Italian carmaker Stellantis

Q55454107 - Fiat Chrysler Automobiles (United Kingdom): company in London, United Kingdom

Q80900581 - Fiat Ireland: automotive company in Ireland

Q21074022 - Fiat Chrysler recalls 7,810 Jeep SUVs for software vulnerability: Wikinews article

Nissan

Q838832 - Caterham Cars: British manufacturer of specialist lightweight sports cars

Q1996975 - Caterham: town in Tandridge district, Surrey, England

Q174008 - Caterham F1: motor racing team

Q2581686 - Caterham railway station: railway station in Caterham, Surrey, England, UK

Nissan

Q27564 - Dodge: American-based brand of automobiles, minivans, and sport utility vehicles and division of Stellantis North America

Q4164135 - Carroll William Dodge: American botanist and mycologist (1895–1988)

Nissan

Q45576845 - Feng Shou: Ming dynasty person CBDB = 307150

Q45586349 - Feng Shou: Ming dynasty person CBDB = 259734

Q102426194 - Feng-shou Ko: Ph.D. University of Pittsburgh 2006

Nissan

Q28993 - GMC: division of General Motors

Q795264 - General Medical Council: healthcare regulator for medical profession in the UK

Q133862584 - GMC: series of geiger counters by GQ Electronics

Q1965281 - GMC CCKW: 2+1⁄2-ton 6×6 Army cargo truck

Q5527647 - Gauhati Medical College and Hospital: medical college in Assam

Nissan

Q25182452 - Hino: village in Morotai Island Regency, North Maluku, Indonesia

Q917187 - Chamaecyparis obtusa: species of plant

Q1208020 - Hino: town in Gamō district, Shiga prefecture, Japan

Q26203563 - Hino: Japanese family name (日野)

Nissan

Q29803 - Isuzu: Japanese company manufacturing motor vehicles

Q3274931 - Japanese cruiser Isuzu: 1921 Nagara-class cruiser

Nissan

Q35594 - Koenigsegg: Swedish manufacturer of high-performance sports cars

Q1147816 - Koenigsegg CCR: mid-engined sports car

Nissan

Q35886 - Lamborghini: Italian car manufacturer

Q318094 - Lamborghini Aventador: car model

Q124966860 - Lamborghini: car brand owned by Automobili Lamborghini S.p.A.

Q98579650 - Lamborghini: tractor manufacturer

Nissan

Q35962 - Maserati: Italian luxury car manufacturer

Q124991042 - Maserati: Italian automobile brand

Q1548973 - Maserati: American post-rock band

Q2666342 - Maserati: Formula One constructor team

Nissan

Q1351854 - McLaren Automotive: British automotive manufacturer

Q172030 - McLaren: British Formula One team

Q131552505 - McLaren: car brand owned by McLaren Automotive Limited

Nissan

Q749239 - Pagani Automobili: Italian car manufacturer

Q29536 - paganism: polytheistic religious groups

Q132560748 - Pagani: car brand owned by Pagani Automobili S.p.A.

Nissan

Q7523322 - Singapore-MIT Alliance for Research and Technology: None

Q22645 - smartphone: handheld mobile telephone/computer

Q620615 - mobile app: software application designed to run on mobile devices

Nissan

Q1533615 - Suzuki: Japanese family name (鈴木)

Q360978 - Suzuki Harunobu: Japanese printmaker

Nissan

Q156578 - Volkswagen Group: German automotive manufacturing conglomerate

Q1766743 - Volkswagen Foundation: private German charitable Foundation

Q1417237 - Volkswagen Bora: Saloon car related to the MK4 Golf.

Q155603 - Volkswagen Arena: football stadium in Wolfsburg, Germany

Nissan

Q26823 - Abarth: Italian racing car maker

Q4573645 - Abarth 1000 GT Coupé: Alfa Romeo car

Q112220966 - Abarth T140: Concept racingcar

Q26234047 - Abarth 124 Spider: Sports car

Nissan

Nissan

Output: None of the candidates

Output: None of the candidates

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Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

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Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


Nissan

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, Beginnings of Datsun brand name from 1914: Masujiro Hashimoto (橋本増治郎) founded theKwaishinsha (Kaishinsha) Motor Car Works(快進社自働車工場,Kwaishinsha jidōsha kōjō;A Good Company Automobile Manufacturer)on 1 July 1911 inAzabu-Hiroodistrict of Tokyo. In 1914, the company produced its first car, called theDAT. The new car's model name was anacronymof the company's investors'surnames: It was renamed toKaishinsha Motorcar Co., Ltd.in 1918, and again toDAT Jidosha & Co., Ltd.(DAT Motorcar Co.) in 1925. DAT Motors built trucks in addition to the DAT and Datsun passenger cars. The vast majority of its output were trucks, due to an almost non-existent consumer market for passenger cars at the time, and disaster recovery efforts as a result of the1923 Great Kantō earthquake. Beginning in 1918, the first DAT trucks were produced for the military market. At the same time,Jitsuyo Jidosha Co., Ltd.(jitsuyo means practical use or utility) produced small trucks using parts, and materials imported from the United States.[better source needed] Commercial operations were placed on hold duringJapan's participation in World War I, and the company contributed to the war effort. In 1926, the Tokyo-based DAT Motors merged with theOsaka-based Jitsuyo Jidosha Co., Ltd(Jitsuyō Jidōsha Seizō Kabushiki-Gaisha)a.k.a.Jitsuyo Jidosha Seizo(established 1919 as aKubotasubsidiary) to becomeDAT Jidosha Seizo Co., Ltd Automobile Manufacturing Co., Ltd.(ダット自動車製造株式会社,DAT Jidōsha Seizō Kabushiki-Gaisha)in Osaka until 1932. From 1923 to 1925, the company produced light cars and trucks under the name of Lila.In 1929, DAT Automobile Manufacturing Inc. merged with a separated part of the manufacturing business ofIHI Corporationto become Automobile Industries Co., Ltd.[clarification needed] In 1931, DAT came out with a new smaller car, called theDatsun Type 11, the first "Datson", meaning "Son of DAT". Later in 1933, afterNissan Groupzaibatsutook control of DAT Motors, the last syllable of Datson was changed to "sun", because "son" also means "loss" in Japanese, hence the name "Datsun"(ダットサン,Dattosan). In 1933, the company name wasNipponizedtoJidosha-Seizo Co., Ltd.(Jidōsha Seizō Kabushiki-Gaisha; "Automobile Manufacturing Share Company")and was moved toYokohama.

[
    "Q27543 - Datsun: automobile brand of Nissan",
    "Q117080876 - Datsun: general cargo vessel built in 2005",
    "Q122948395 - Datsun: Norwegian musical group",
    "Q1664019 - The Datsuns: hard rock band from Cambridge, New Zealand",
    "Q846552 - Datsun Truck: pickup"
]

[
    "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)"
]

[
    "Q377180 - Datsun Type 11: car model"
]

[
    "Q25384144 - Jitsuyō Jidōsha Seizō: None"
]

[
    "Q25384144 - Jitsuyō Jidōsha Seizō: None"
]

[
    "Q274498 - 1923 Great Kantō Earthquake: earthquake",
    "Q3742341 - aftermath of 1923 Great Kantō earthquake: None",
    "Q3528897 - 1923 Great Kantō earthquake issue: None"
]

[
    "Q361 - World War I: global war originating in Europe, 1914–1918",
    "Q362 - World War II: 1939–1945 global conflict",
    "Q3823675 - World War I: 2005 video game",
    "Q626006 - World War II in Yugoslavia: part of the European theater of World War II",
    "Q2079534 - World War II Victory Medal: American service medal"
]

[
    "Q1653679 - IHI Corporation: manufacturing company in Japan",
    "Q29122978 - IHI Corporation (United States): None",
    "Q2159756 - IHI Corporation F7: 2000s Japanese turbofan aircraft engine",
    "Q11254783 - IHI Corporation XF5: 2010s Japanese turbofan aircraft engine",
    "Q60807097 - IHI Corporation XF9: 2010s Japanese turbofan aircraft engine"
]

[
    "Q27543 - Datsun: automobile brand of Nissan",
    "Q117080876 - Datsun: general cargo vessel built in 2005",
    "Q122948395 - Datsun: Norwegian musical group",
    "Q1664019 - The Datsuns: hard rock band from Cambridge, New Zealand",
    "Q846552 - Datsun Truck: pickup"
]

[
    "Q38283 - Yokohama: designated city in Kanagawa Prefecture, Japan",
    "Q1205910 - Yokohama: town in Kamikita district, Aomori prefecture, Japan",
    "Q801658 - Yokohama Station: railway station and metro station in Nishi-ku, Yokohama, Kanagawa prefecture, Japan",
    "Q8054634 - Yokohama International Swimming Pool: building in Tsuzuki-ku, Kanagawa Prefecture, Japan",
    "Q1192619 - Yokohama National University: Japanese national university"
]

[
    "Q1588427 - Masujiro Hashimoto: Japanese mechanical engineer (1875–1944)"
]

[
    "Q274498 - 1923 Great Kantō Earthquake: earthquake",
    "Q24895832 - Great Kanto Earthquake Memorial Museum: None",
    "Q119651179 - Great Kanto Earthquake of Japan and the Aid from Chinese Government and People: scholarly article in Hebei Academic Journal, no. 4, 2011"
]

[
    "Q4115848 - Nissan Concern: Japanese conglomerate"
]

[
    "Q69258450 - July 1, 1911: date in Gregorian calendar"
]

[
    "Q1490 - Tokyo: capital and largest city of Japan",
    "Q7473516 - Tokyo: special wards in the eastern part of Tokyo Metropolis in Japan, that used to form a single city",
    "Q41187 - Sony: Japanese multinational conglomerate corporation",
    "Q7813796 - Tokyo: Masaharu Fukuyama song",
    "Q7842 - University of Tokyo: national university in Tokyo, Japan"
]

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


Nissan

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, Beginnings of Datsun brand name from 1914: Masujiro Hashimoto (橋本増治郎) founded theKwaishinsha (Kaishinsha) Motor Car Works(快進社自働車工場,Kwaishinsha jidōsha kōjō;A Good Company Automobile Manufacturer)on 1 July 1911 inAzabu-Hiroodistrict of Tokyo. In 1914, the company produced its first car, called theDAT. The new car's model name was anacronymof the company's investors'surnames: It was renamed toKaishinsha Motorcar Co., Ltd.in 1918, and again toDAT Jidosha & Co., Ltd.(DAT Motorcar Co.) in 1925. DAT Motors built trucks in addition to the DAT and Datsun passenger cars. The vast majority of its output were trucks, due to an almost non-existent consumer market for passenger cars at the time, and disaster recovery efforts as a result of the1923 Great Kantō earthquake. Beginning in 1918, the first DAT trucks were produced for the military market. At the same time,Jitsuyo Jidosha Co., Ltd.(jitsuyo means practical use or utility) produced small trucks using parts, and materials imported from the United States.[better source needed] Commercial operations were placed on hold duringJapan's participation in World War I, and the company contributed to the war effort. In 1926, the Tokyo-based DAT Motors merged with theOsaka-based Jitsuyo Jidosha Co., Ltd(Jitsuyō Jidōsha Seizō Kabushiki-Gaisha)a.k.a.Jitsuyo Jidosha Seizo(established 1919 as aKubotasubsidiary) to becomeDAT Jidosha Seizo Co., Ltd Automobile Manufacturing Co., Ltd.(ダット自動車製造株式会社,DAT Jidōsha Seizō Kabushiki-Gaisha)in Osaka until 1932. From 1923 to 1925, the company produced light cars and trucks under the name of Lila.In 1929, DAT Automobile Manufacturing Inc. merged with a separated part of the manufacturing business ofIHI Corporationto become Automobile Industries Co., Ltd.[clarification needed] In 1931, DAT came out with a new smaller car, called theDatsun Type 11, the first "Datson", meaning "Son of DAT". Later in 1933, afterNissan Groupzaibatsutook control of DAT Motors, the last syllable of Datson was changed to "sun", because "son" also means "loss" in Japanese, hence the name "Datsun"(ダットサン,Dattosan). In 1933, the company name wasNipponizedtoJidosha-Seizo Co., Ltd.(Jidōsha Seizō Kabushiki-Gaisha; "Automobile Manufacturing Share Company")and was moved toYokohama.

Datsun

Q27543 - Datsun: automobile brand of Nissan

Q117080876 - Datsun: general cargo vessel built in 2005

Q122948395 - Datsun: Norwegian musical group

Q1664019 - The Datsuns: hard rock band from Cambridge, New Zealand

Q1588427 - Masujiro Hashimoto: Japanese mechanical engineer (1875–1944)

Datsun

Datsun

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)

Datsun

Datsun

Datsun

Q25384746 - Jidōsha Kōgyō: former company in Japan

Datsun

Datsun

Datsun

Datsun

Q1653679 - IHI Corporation: manufacturing company in Japan

Q29122978 - IHI Corporation (United States): None

Q2159756 - IHI Corporation F7: 2000s Japanese turbofan aircraft engine

Q11254783 - IHI Corporation XF5: 2010s Japanese turbofan aircraft engine

Q60807097 - IHI Corporation XF9: 2010s Japanese turbofan aircraft engine

Datsun

Datsun

P248 - stated in: to be used in the references field to refer to the information document or database in which a claim is made; for qualifiers use P805; for the type of document in which a claim is made use P3865

P577 - publication date: date or point in time when a work was first published or released

P1476 - title: published name of a work, such as a newspaper article, a literary work, piece of music, a website, or a performance work

P1932 - object named as: use as qualifier to indicate how the object's value was given in the source

P495 - country of origin: country of origin of this item (creative work, food, phrase, product, etc.)

Q17 - Japan: island country in East Asia

Q13989 - Honshu: largest island of Japan

Q5287 - Japanese: language spoken in East Asia

Q188712 - Empire of Japan: Empire in the Asia-Pacific region from 1868 to 1947

Q205662 - Tokugawa shogunate: 1603–1868 Japanese military government

Datsun

Datsun

Datsun

Q1490 - Tokyo: capital and largest city of Japan

Q7473516 - Tokyo: special wards in the eastern part of Tokyo Metropolis in Japan, that used to form a single city

Q41187 - Sony: Japanese multinational conglomerate corporation

Q7813796 - Tokyo: Masaharu Fukuyama song

Q7842 - University of Tokyo: national university in Tokyo, Japan

Datsun

Datsun

Q26229941 - Azabu: Japanese family name (麻生)

Q20042944 - Azabu: dissolved municipality in Nishikamo district, Aichi prefecture, Japan

Q11677479 - Azabu-ku: dissolved municipality in Tokyo, Japan

Datsun

Datsun

Q122723 - Osaka Prefecture: prefecture of Japan

Q35765 - Osaka: designated city in Osaka Prefecture, Japan

Q11439906 - Ōsaka: refers to central area of Osaka City, or Osaka City, Osaka prefecture

Q61609759 - Osaka: genus of annelids in the order Haplotaxida

Datsun

Datsun

Q274498 - 1923 Great Kantō Earthquake: earthquake

Q3742341 - aftermath of 1923 Great Kantō earthquake: None

Q3528897 - 1923 Great Kantō earthquake issue: None

Datsun

Datsun

Q361 - World War I: global war originating in Europe, 1914–1918

Q362 - World War II: 1939–1945 global conflict

Q626006 - World War II in Yugoslavia: part of the European theater of World War II

Q2079534 - World War II Victory Medal: American service medal

Datsun

Datsun

Datsun

Datsun

Datsun

Datsun

Q38283 - Yokohama: designated city in Kanagawa Prefecture, Japan

Q1205910 - Yokohama: town in Kamikita district, Aomori prefecture, Japan

Q801658 - Yokohama Station: railway station and metro station in Nishi-ku, Yokohama, Kanagawa prefecture, Japan

Q8054634 - Yokohama International Swimming Pool: building in Tsuzuki-ku, Kanagawa Prefecture, Japan

Q1192619 - Yokohama National University: Japanese national university

Datsun

Datsun

Datsun

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


Nissan

Nissan name debut

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 name debut: In 1928,Yoshisuke Aikawa(nickname: Gisuke/Guisuke Ayukawa) founded theholding companyNihon Sangyo(日本産業 Japan Industries or Nihon Industries). The name 'Nissan' originated during the 1930s as an abbreviationused on theTokyo Stock ExchangeforNihonSangyo. This company wasNissan"Zaibatsu" which included Tobata Casting andHitachi. At this time Nissan controlled foundries and auto parts businesses, but Aikawa did not enter automobile manufacturing until 1933. The zaibatsu eventually grew to include 74 firms and became the fourth-largest in Japan duringWorld War II.In 1931, DAT Jidosha Seizo became affiliated withTobata Castingand was merged into Tobata Casting in 1933. As Tobata Casting was a Nissan company, this was the beginning of Nissan's automobile manufacturing.

[
    "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)"
]

[
    "Q404867 - Yoshisuke Aikawa: Japanese entrepreneur and businessman (1880-1967)"
]

[
    "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)"
]

[
    "Q19715 - 1928: year",
    "Q19241571 - 1928: natural number",
    "Q8138 - 1928 Summer Olympics: Games of the IX Olympiad, in Amsterdam, The Netherlands",
    "Q9591 - 1928 Winter Olympics: 2nd edition of Winter Olympics, in Sankt Moritz, Switzerland",
    "Q53384246 - 1928-1929 one-year-period: period of about one year starting in 1928 and ending in 1929"
]

[
    "Q774901 - Japanese Industrial Standards: technical standard",
    "Q112270109 - Nihon Sangyō: business",
    "Q118143374 - Japan Industrial Design Promotion Organization: None"
]

[
    "Q362 - World War II: 1939–1945 global conflict",
    "Q15053459 - World War II: 1995 edition by the Military Book Club",
    "Q2631997 - Theatre of War: 2006 video game developed by 1C Company",
    "Q626006 - World War II in Yugoslavia: part of the European theater of World War II",
    "Q2079534 - World War II Victory Medal: American service medal"
]

[
    "Q713143 - 74: natural number",
    "Q743 - tungsten: chemical element with symbol W and atomic number 74",
    "Q30412 - 74: year",
    "Q940611 - 743: natural number",
    "Q156855 - 74 Galatea: main-belt asteroid"
]

[
    "Q18726 - 1933: year",
    "Q19241578 - 1933: natural number",
    "Q53384368 - 1933-1934 one-year-period: period of about one year starting in 1933 and ending in 1934",
    "Q44069874 - 1933: Ethiopian year",
    "Q98925571 - 1933-1934 Massachusetts legislature: legislative session in US state of Massachusetts"
]

[
    "Q217475 - Tokyo Stock Exchange: stock market based in Tokyo",
    "Q1683760 - Tokyo Stock Exchange Jasdaq: former stock exchange in Japan",
    "Q11249728 - Tokyo Stock Exchange-Tokyo Pro Market: Registered market for equities in Japan",
    "Q107190259 - Tokyo Stock Exchange - Tostnet-1: None",
    "Q107190261 - Tokyo Stock Exchange - Tostnet-3: Tokyo Stock Exchange's Tostnet-3 market is an off-auction market designed for issuer's share buyback."
]

[
    "Q11491418 - DAT Jidōsha Seizō: None"
]

Output: None of the candidates


Nissan

Nissan name debut

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 name debut: In 1928,Yoshisuke Aikawa(nickname: Gisuke/Guisuke Ayukawa) founded theholding companyNihon Sangyo(日本産業 Japan Industries or Nihon Industries). The name 'Nissan' originated during the 1930s as an abbreviationused on theTokyo Stock ExchangeforNihonSangyo. This company wasNissan"Zaibatsu" which included Tobata Casting andHitachi. At this time Nissan controlled foundries and auto parts businesses, but Aikawa did not enter automobile manufacturing until 1933. The zaibatsu eventually grew to include 74 firms and became the fourth-largest in Japan duringWorld War II.In 1931, DAT Jidosha Seizo became affiliated withTobata Castingand was merged into Tobata Casting in 1933. As Tobata Casting was a Nissan company, this was the beginning of Nissan's automobile manufacturing.

Nissan

Q404867 - Yoshisuke Aikawa: Japanese entrepreneur and businessman (1880-1967)

Nissan

Q8138 - 1928 Summer Olympics: Games of the IX Olympiad, in Amsterdam, The Netherlands

Q9591 - 1928 Winter Olympics: 2nd edition of Winter Olympics, in Sankt Moritz, Switzerland

Q53384246 - 1928-1929 one-year-period: period of about one year starting in 1928 and ending in 1929

Nissan

Nissan

Q774901 - Japanese Industrial Standards: technical standard

Q118143374 - Japan Industrial Design Promotion Organization: None

Nihon_Sangyo

P1449 - nickname: informal name (for a pseudonym use P742)

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_Zaibatsu

Q53238 - Hitachi: Japanese multinational engineering and electronics company

Q633922 - Hitachi: city in Ibaraki Prefecture, Japan

Q20108946 - Splendor: former Belgian cycling team (1975-1989)

Q1055353 - Hitachi Province: province of Japan

Q1040139 - Hitachi Rail STS: Wholly owned subsidiary of Hitachi.

Nissan_Zaibatsu

Nissan_Zaibatsu

Q743 - tungsten: chemical element with symbol W and atomic number 74

Q156855 - 74 Galatea: main-belt asteroid

Nissan

Q362 - World War II: 1939–1945 global conflict

Q15053459 - World War II: 1995 edition by the Military Book Club

Q2631997 - Theatre of War: 2006 video game developed by 1C Company

Q626006 - World War II in Yugoslavia: part of the European theater of World War II

Q2079534 - World War II Victory Medal: American service medal

Nissan

Nissan

Q918375 - 1931 United Kingdom general election: general election in the UK

Q21084453 - 36th Parliament of the United Kingdom: Parliament of the United Kingdom, 1931-1935

Nissan

Q53384368 - 1933-1934 one-year-period: period of about one year starting in 1933 and ending in 1934

Q98925571 - 1933-1934 Massachusetts legislature: legislative session in US state of Massachusetts

Nissan

Nissan

Q217475 - Tokyo Stock Exchange: stock market based in Tokyo

Q1683760 - Tokyo Stock Exchange Jasdaq: former stock exchange in Japan

Q11249728 - Tokyo Stock Exchange-Tokyo Pro Market: Registered market for equities in Japan

Q107190259 - Tokyo Stock Exchange - Tostnet-1: None

Q107190261 - Tokyo Stock Exchange - Tostnet-3: Tokyo Stock Exchange's Tostnet-3 market is an off-auction market designed for issuer's share buyback.

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


Nissan

Nissan Motor

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 Motor: In 1934, Aikawa separated the expanded automobile parts division of Tobata Casting and incorporated it as a new subsidiary, which he namedNissan Motor Co., Ltd.(日産自動車,Nissan Jidōsha).The shareholders of the new company; however, were not enthusiastic about the prospects of the automobile in Japan, so Aikawa bought out all the Tobata Casting shareholders (using capital from Nihon Industries) in June 1934. At this time, Nissan Motor effectively became owned byNihon Sangyo and Hitachi. In 1935, the construction of its Yokohama plant was completed. 44 Datsuns were shipped to Asia, Central and South America. In 1935, the first car manufactured by anintegrated assembly systemrolled off the line at the Yokohama plant.Nissan built trucks, airplanes, and engines for theImperial Japanese Army. In November 1937 Nissan moved its headquarters toXinjing, the capital ofManchukuo. In December the company changed its name toManchuria Heavy Industries Developing Co(MHID). In 1940, the first knockdown kits were shipped toDowa Jidosha Kogyo (Dowa Automobile), one of MHID's companies, for assembly.In 1944, the head office was moved toNihonbashi, Tokyo, and the company name was changed to Nissan Heavy Industries, Ltd., which the company kept through 1949.

[
    "Q20165 - Nissan Motor Co. Ltd.: Japanese company",
    "Q1543585 - Nismo: motorsport and performance division of automobile manufacturer Nissan",
    "Q1993716 - Nissan Motor Ibérica: Spanish subsidiary company",
    "Q11509889 - Nissan Motor Co. Baseball Club: None",
    "Q9544829 - Nissan Motor Indonesia: Indonesian subsidiary company"
]

[
    "Q18714 - 1934: year",
    "Q19241579 - 1934: natural number",
    "Q1477177 - 1934 FIFA World Cup: 2nd FIFA World Cup, held in Italy",
    "Q44069556 - 1934: Ethiopian year",
    "Q3597882 - 1934: 1982 novel by Alberto Moravia"
]

[
    "Q16147822 - Aikawa: family name",
    "Q80368797 - Masamichi Aikawa: Japanese parasitologist",
    "Q1203267 - Aikawa: town in Aikō district, Kanagawa prefecture, Japan",
    "Q11582006 - Aikawa: dissolved municipality in Sado district, Niigata prefecture, Japan",
    "Q26229567 - Aikawa: Japanese family name (相川)"
]

[
    "Q20165 - Nissan Motor Co. Ltd.: Japanese company",
    "Q1543585 - Nismo: motorsport and performance division of automobile manufacturer Nissan",
    "Q1993716 - Nissan Motor Ibérica: Spanish subsidiary company",
    "Q11509889 - Nissan Motor Co. Baseball Club: None",
    "Q9544829 - Nissan Motor Indonesia: Indonesian subsidiary company"
]

[
    "Q774901 - Japanese Industrial Standards: technical standard",
    "Q112270109 - Nihon Sangyō: business",
    "Q118143374 - Japan Industrial Design Promotion Organization: None"
]

[
    "Q53238 - Hitachi: Japanese multinational engineering and electronics company",
    "Q633922 - Hitachi: city in Ibaraki Prefecture, Japan",
    "Q20108946 - Splendor: former Belgian cycling team (1975-1989)",
    "Q1055353 - Hitachi Province: province of Japan",
    "Q1040139 - Hitachi Rail STS: Wholly owned subsidiary of Hitachi."
]

[
    "Q38283 - Yokohama: designated city in Kanagawa Prefecture, Japan",
    "Q1205910 - Yokohama: town in Kamikita district, Aomori prefecture, Japan",
    "Q801658 - Yokohama Station: railway station and metro station in Nishi-ku, Yokohama, Kanagawa prefecture, Japan",
    "Q8054634 - Yokohama International Swimming Pool: building in Tsuzuki-ku, Kanagawa Prefecture, Japan",
    "Q1192619 - Yokohama National University: Japanese national university"
]

[
    "Q712706 - 44: natural number",
    "Q87533388 - ㊹: Unicode character",
    "Q1086 - ruthenium: chemical element with symbol Ru and atomic number 44",
    "Q30243 - 44: year",
    "Q277564 - 449: natural number"
]

[
    "Q27543 - Datsun: automobile brand of Nissan",
    "Q117080876 - Datsun: general cargo vessel built in 2005",
    "Q122948395 - Datsun: Norwegian musical group",
    "Q1664019 - The Datsuns: hard rock band from Cambridge, New Zealand",
    "Q846552 - Datsun Truck: pickup"
]

[
    "Q30623 - Manchukuo: Japanese puppet state in Manchuria (1932–1945)",
    "Q681179 - Manchukuo Imperial Army: Ground forces of Manchukuo",
    "Q696612 - Manchukuo yuan: currency",
    "Q698060 - Manchukuo Film Association: japanese-Manchurian film studio",
    "Q699661 - Manchukuo Imperial Navy: navy"
]

[
    "Q92161 - Changchun: capital city of Jilin Province, China",
    "Q11084528 - Xinjing: town in Guangxi, People's Republic of China",
    "Q31852053 - Xinjing: town in Guizhou, People's Republic of China",
    "Q11083172 - Xinjing Town: town in Changning District, Shanghai",
    "Q113651833 - Xinjing: former county of China"
]

[
    "Q18633 - 1940: year",
    "Q12681686 - 1940: natural number",
    "Q36561 - 1940s: decade of the Gregorian calendar (1940–1949)",
    "Q2811748 - 1940 in association football: association football-related events during 1940",
    "Q53384548 - 1940-1941 one-year-period: period of about one year starting in 1940 and ending in 1941"
]

[
    "Q1141952 - Nihonbashi: town located in Chūō-ku, Tokyo",
    "Q927789 - Nihonbashi-ku: dissolved municipality in Tokyo, Japan",
    "Q75098535 - Nihonbashi: Starting point of the Edo Five Routes",
    "Q100930819 - Nihonbashi: bridge located in Tokyo, Japan",
    "Q11508312 - Nihonbashi: play by Kyōka Izumi"
]

[
    "Q5188 - 1949: year",
    "Q12681687 - 1949: natural number",
    "Q314561 - 1949 West German presidential election: election in West Germany",
    "Q695222 - 1949 West German federal election: Federal elections held in West Germany on 14 August 1949",
    "Q53384788 - 1949-1950 one-year-period: period of about one year starting in 1949 and ending in 1950"
]

[
    "Q276535 - Imperial Japanese Army: ground-based armed forces of Japan, from 1868 to 1945",
    "Q700130 - Imperial Japanese Army Academy: Japanese military academy (1874-1945)",
    "Q702107 - Imperial Japanese Air Force: aerial warfare branch of the Imperial Japanese Army",
    "Q702605 - Army War College: staff college of the Imperial Japanese Army",
    "Q66737903 - Imperial Japanese Army infantry division: military unit type and size designation within the Imperial Japanese Army"
]


Nissan

Nissan Motor

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 Motor: In 1934, Aikawa separated the expanded automobile parts division of Tobata Casting and incorporated it as a new subsidiary, which he namedNissan Motor Co., Ltd.(日産自動車,Nissan Jidōsha).The shareholders of the new company; however, were not enthusiastic about the prospects of the automobile in Japan, so Aikawa bought out all the Tobata Casting shareholders (using capital from Nihon Industries) in June 1934. At this time, Nissan Motor effectively became owned byNihon Sangyo and Hitachi. In 1935, the construction of its Yokohama plant was completed. 44 Datsuns were shipped to Asia, Central and South America. In 1935, the first car manufactured by anintegrated assembly systemrolled off the line at the Yokohama plant.Nissan built trucks, airplanes, and engines for theImperial Japanese Army. In November 1937 Nissan moved its headquarters toXinjing, the capital ofManchukuo. In December the company changed its name toManchuria Heavy Industries Developing Co(MHID). In 1940, the first knockdown kits were shipped toDowa Jidosha Kogyo (Dowa Automobile), one of MHID's companies, for assembly.In 1944, the head office was moved toNihonbashi, Tokyo, and the company name was changed to Nissan Heavy Industries, Ltd., which the company kept through 1949.

Nissan_Motor

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

Q1993716 - Nissan Motor Ibérica: Spanish subsidiary company

Q11509889 - Nissan Motor Co. Baseball Club: None

Q9544829 - Nissan Motor Indonesia: Indonesian subsidiary company

Nissan_Motor

Nissan_Motor

Nissan_Motor

P36 - capital: seat of government of a country, province, state or other type of administrative territorial entity

P1376 - capital of: country, state, department, canton or other administrative division of which the municipality is the governmental seat

P2130 - capital cost: amount of money spent on building or producing an object

P6106 - uses capitalization for: lexical classes and subclasses of words as well as other uses in the language where the first letter is written as a capital letter

P2218 - net worth: totality of wealth possessed by a person

Nissan_Motor

Q53238 - Hitachi: Japanese multinational engineering and electronics company

Q633922 - Hitachi: city in Ibaraki Prefecture, Japan

Q20108946 - Splendor: former Belgian cycling team (1975-1989)

Q1055353 - Hitachi Province: province of Japan

Q1040139 - Hitachi Rail STS: Wholly owned subsidiary of Hitachi.

Nissan_Motor

Q17 - Japan: island country in East Asia

Q13989 - Honshu: largest island of Japan

Q5287 - Japanese: language spoken in East Asia

Q188712 - Empire of Japan: Empire in the Asia-Pacific region from 1868 to 1947

Q205662 - Tokugawa shogunate: 1603–1868 Japanese military government

Nissan_Motor

P355 - child organization/unit: child organization/unit or subsidiary of a company or organization/unit; generally a fully owned separate corporation. Compare with "business division" (P199). Opposite of parent organization/unit (P749).

P749 - parent organization: parent organization of an organization, opposite of subsidiaries (P355)

Q27543 - Datsun: automobile brand of Nissan

Q117080876 - Datsun: general cargo vessel built in 2005

Q122948395 - Datsun: Norwegian musical group

Q1664019 - The Datsuns: hard rock band from Cambridge, New Zealand

Nissan_Motor

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

Q1420 - car: motorized road vehicle designed to carry one to eight people rather than primarily goods

Q5386 - auto racing: motorsport involving the racing of cars for competition

Q188509 - suburb: human settlement that is part of or near to a larger city or town

Q3231690 - automobile model: industrial automobile model associated with a brand, defined usually from an engineering point of view by a combination of chassis/bodywork

Q10349745 - racing automobile driver: occupation driving an automobile in competition

Nissan_Motor

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

P361 - part of: object of which the subject is a part (if this subject is already part of object A which is a part of object B, then please only make the subject part of object A), inverse property of "has part" (P527, see also "has parts of the class" (P2670))

P4333 - GenBank assembly accession number: unique identifier of a genome assembly

Q38283 - Yokohama: designated city in Kanagawa Prefecture, Japan

Q1205910 - Yokohama: town in Kamikita district, Aomori prefecture, Japan

Q801658 - Yokohama Station: railway station and metro station in Nishi-ku, Yokohama, Kanagawa prefecture, Japan

Q8054634 - Yokohama International Swimming Pool: building in Tsuzuki-ku, Kanagawa Prefecture, Japan

Q1192619 - Yokohama National University: Japanese national university

Nissan_Motor

Nissan_Motor

Q1477177 - 1934 FIFA World Cup: 2nd FIFA World Cup, held in Italy

Q3597882 - 1934: 1982 novel by Alberto Moravia

Nissan_Motor

P159 - headquarters location: city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building

Q1141952 - Nihonbashi: town located in Chūō-ku, Tokyo

Q927789 - Nihonbashi-ku: dissolved municipality in Tokyo, Japan

Q75098535 - Nihonbashi: Starting point of the Edo Five Routes

Q100930819 - Nihonbashi: bridge located in Tokyo, Japan

Q11508312 - Nihonbashi: play by Kyōka Izumi

Nissan_Motor

Q43193 - truck: commercial or utilitarian large motor vehicle

Q506402 - Truckee: town in Nevada County, California, United States

Q135053025 - Truck: 2024 video game developed by Molance Games

Q217421 - bogie: framework which carries a wheelset and is attached to a rail vehicle

Q508846 - truck driver: person who earns a living as the driver of a truck

Nissan_Motor

Q197 - airplane: powered fixed-wing aircraft

Q10403873 - Airplane: 1998 studio album by Arvingarna

Q15056995 - aircraft model: specific aircraft design and specification, differentiated from others of its family

Q135051554 - Airplane: 2024 video game developed by Sulaiman Jackson, Ryan Leftridge and Austin Morgan

Q192375 - hangar: closed structure to hold aircraft or spacecraft in protective storage

Nissan_Motor

Q96329007 - Engineering Mechanics: journal

Q93301 - locomotive: railway vehicle that provides the motive power for a train

Q44167 - engine: machine designed to produce mechanical energy from another form of energy

Q81096 - engineer: professional practitioner of engineering

Nissan_Motor

Nissan_Motor

Nissan_Motor

Q92161 - Changchun: capital city of Jilin Province, China

Q11084528 - Xinjing: town in Guangxi, People's Republic of China

Q31852053 - Xinjing: town in Guizhou, People's Republic of China

Q11083172 - Xinjing Town: town in Changning District, Shanghai

Q113651833 - Xinjing: former county of China

Nissan_Motor

Nissan_Motor

Q314561 - 1949 West German presidential election: election in West Germany

Q695222 - 1949 West German federal election: Federal elections held in West Germany on 14 August 1949

Q53384788 - 1949-1950 one-year-period: period of about one year starting in 1949 and ending in 1950

Nissan_Motor

Nissan_Motor

Q30623 - Manchukuo: Japanese puppet state in Manchuria (1932–1945)

Q681179 - Manchukuo Imperial Army: Ground forces of Manchukuo

Q698060 - Manchukuo Film Association: japanese-Manchurian film studio

Nissan_Motor

Q743004 - aircraft engine: engine designed for use in powered aircraft

Q4828699 - aeronautical engineering: science and technology of designing, developing and assembling of aircraft, airports and aerodromes

Q53952288 - Aircraft Engineering and Aerospace Technology: scientific journal

Q618728 - Glenn Research Center: NASA research center in Ohio, US

Q122751267 - Aircraft engine: US patent 11525408

Nissan_Motor

Q276535 - Imperial Japanese Army: ground-based armed forces of Japan, from 1868 to 1945

Q700130 - Imperial Japanese Army Academy: Japanese military academy (1874-1945)

Q702107 - Imperial Japanese Air Force: aerial warfare branch of the Imperial Japanese Army

Q702605 - Army War College: staff college of the Imperial Japanese Army

Q66737903 - Imperial Japanese Army infantry division: military unit type and size designation within the Imperial Japanese Army

Nissan_Motor

Q1490 - Tokyo: capital and largest city of Japan

Q7473516 - Tokyo: special wards in the eastern part of Tokyo Metropolis in Japan, that used to form a single city

Q41187 - Sony: Japanese multinational conglomerate corporation

Q7813796 - Tokyo: Masaharu Fukuyama song

Q7842 - University of Tokyo: national university in Tokyo, Japan

Nissan_Motor

Q774901 - Japanese Industrial Standards: technical standard

Q118143374 - Japan Industrial Design Promotion Organization: None

Nissan_Motor

Nissan_Motor

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_Motor

Q906512 - shipwrecking: event causing a ship to wreck

Q852190 - shipwreck: remains of a ship that has wrecked

Q56351075 - ship name: name assigned to an individual ship, often in a special naming ceremony

Nissan_Motor

Nissan_Motor

Nissan_Motor

Nissan_Motor

Nissan_Motor

Nissan_Motor

Nissan_Motor

Nissan_Motor

Nissan_Motor

Nissan_Motor

Nissan_Motor

Nissan_Motor

Nissan_Motor

Nissan_Motor

Nissan_Motor

Nissan_Motor

Nissan_Motor

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


Nissan

American market and expansion

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, American market and expansion: DAT had inheritedKubota's chief designer, American engineerWilliam R. Gorham. This, along with Aikawa's 1908 visit to Detroit, was to greatly affect Nissan's future.Although it had always been Aikawa's intention to use cutting-edge auto making technology from America, it was Gorham that carried out the plan. Most of the machinery and processes originally came from the United States. When Nissan started to assemble larger vehicles under the "Nissan" brand in 1937, much of the design plans and plant facilities were supplied by theGraham-PaigeCompany.Nissan also had a Graham license under which passenger cars, buses, and trucks were made. In his 1986 bookThe Reckoning,David Halberstamstates "In terms of technology, Gorham was the founder of the Nissan Motor Company" and that "young Nissan engineers who had never met him spoke of him as a god and could describe in detail his years at the company and his many inventions."

[
    "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)"
]

[
    "Q2580296 - William R. Gorham: Japanese engineer (1888–1949)"
]

[
    "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)"
]

[
    "Q30 - United States: country located primarily in North America",
    "Q637413 - United States Census Bureau: U.S. agency responsible for the census and related statistics",
    "Q11703 - United States Virgin Islands: unincorporated territory of the United States of America",
    "Q4917 - United States dollar: official currency of the United States",
    "Q214867 - National Gallery of Art: national art museum in Washington, D.C."
]

[
    "Q180003 - American National Standards Institute: American standards development organization",
    "Q15813992 - American Engineer and Railroad Journal: American magazine, published 1893–1913",
    "Q4743684 - American Engineers' Council for Professional Development: organization",
    "Q61934404 - American Engineering & Manufacturing (United States): company in Elyria, United States",
    "Q61934410 - American Engineering Group (United States): company in Akron, United States"
]

[
    "Q281079 - Gorham: town in Cumberland County, Maine, United States",
    "Q48386771 - Gorham Manufacturing Company: American silver manufactory, founded 1831",
    "Q29547227 - Gorham: family name",
    "Q966159 - Gorham: town in New Hampshire, United States",
    "Q47491953 - Gorham: given name"
]

[
    "Q1709856 - Journal of Marketing: Academic journal",
    "Q4744398 - American Market: None",
    "Q465622 - American Marketing Association: professional association for marketeers",
    "Q28152172 - American Marketing Journal: journal 1934-1936",
    "Q74797880 - American Marketing Association launches new healthcare council: scientific article published on 01 October 1994"
]

[
    "Q145599 - dative case: grammatical case generally used to indicate the noun to which something is given",
    "Q821542 - DataCite: international not-for-profit organization which aims to improve data citation",
    "Q50358336 - datos.bne.es: open data platform of the Biblioteca Nacional de España",
    "Q9683 - Tang dynasty: empire in the China proper between 618 and 907 AD",
    "Q19938912 - BnF authorities: authority file for persons, organisations, works, topics, and geographic places, of the French National Library"
]

[
    "Q1541900 - Graham-Paige: company",
    "Q5592611 - Graham-Paige 835: None"
]

[
    "Q16147822 - Aikawa: family name",
    "Q80368797 - Masamichi Aikawa: Japanese parasitologist",
    "Q1203267 - Aikawa: town in Aikō district, Kanagawa prefecture, Japan",
    "Q11582006 - Aikawa: dissolved municipality in Sado district, Niigata prefecture, Japan",
    "Q26229567 - Aikawa: Japanese family name (相川)"
]

[
    "Q12439 - Detroit: city in and county seat of Wayne County, State of Michigan, United States",
    "Q1201549 - Detroit Institute of Arts: art museum in Detroit, Michigan",
    "Q271880 - Detroit Lions: National Football League franchise in Detroit, Michigan",
    "Q21246348 - Detroit: Become Human: 2018 adventure video game developed by Quantic Dream",
    "Q650855 - Detroit Tigers: baseball team and Major League Baseball franchise in Detroit, Michigan, United States of America"
]

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates