Nissan

American market and expansion

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, 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."

Nissan

P737 - influenced by: the subject (person, idea, etc.) was influenced or inspired by this object entity, e.g. "Heidegger was influenced by Aristotle"

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

Nissan

Q1541900 - Graham-Paige: company
Q5592611 - Graham-Paige 835: None

Nissan

Nissan

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.

Nissan

Nissan

Q281079 - Gorham: town in Cumberland County, Maine, United States

Q48386771 - Gorham Manufacturing Company: American silver manufactory, founded 1831

Q966159 - Gorham: town in New Hampshire, United States

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

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, Austin Motor Company relations (1937–1960s): From 1934, Datsun began to buildAustin 7sunder license. This operation became the greatest success ofAustin's overseas licensing of its Seven and marked the beginning of Datsun's international success. In 1952, Nissan entered into a legal agreement with Austin,for Nissan to assemble 2,000 Austins from imported partially assembled sets and sell them in Japan under the Austin trademark. The agreement called for Nissan to make all Austin parts locally within three years, a goal Nissan met. Nissan produced and marketed Austins for seven years. The agreement also gave Nissan the rights to use Austin patents, which Nissan used in developing its own engines for itsDatsunline of cars. In 1953, British-built Austins were assembled and sold, but by 1955, theAustin A50– completely built by Nissan and featuring a new 1489 cc engine — was on the market in Japan. Nissan produced 20,855 Austins from 1953 to 1959. Nissan leveraged the Austin patents to further develop its own modern engine designs beyond what Austin'sA-andB-familydesigns offered. The apex of the Austin-derived engines was the new designA series enginein 1966. In 1967, Nissan introduced its new highly advanced four-cylinder overhead cam (OHC)Nissan L engine, which while similar toMercedes-BenzOHC designs was a totally new engine designed by Nissan. This engine powered the newDatsun 510, which gained Nissan respect in the worldwidesedanmarket. Then, in 1969, Nissan introduced theDatsun 240Zsports car which used a six-cylinder variation of theL seriesengine, developed under Nissan Machinery (Nissan Koki Co., Ltd.日産工機) in 1964, a former remnant of another auto manufacturerKurogane. The240Zwas an immediate sensation and lifted Nissan to world-class status in the automobile market. During theKorean War, Nissan was a major vehicle producer for the US Army.After the Korean War ended, significant levels of anti-communist sentiment existed in Japan. The union that organized Nissan's workforce was strong and militant.Nissan was in financial difficulties, and when wage negotiations came, the company took a hard line. Workers were locked out, and several hundred were fired. The Japanese government and the US occupation forces arrested several union leaders.The union ran out of strike funds and was defeated. A new labor union was formed,with Shioji Ichiro one of its leaders. Ichiro had studied atHarvard Universityon a US government scholarship. He advanced an idea to trade wage cuts against saving 2,000 jobs.Ichiro's idea was made part of a new union contractthat prioritized productivity. Between 1955 and 1973, Nissan "expanded rapidly on the basis of technical advances supported – and often suggested – by the union." Ichiro became president of theConfederation of Japan Automobile Workers' Unionsand "the most influential figure in the right wing of the Japanese labor movement."

[
    "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"
]

[
    "Q781156 - Austin Motor Company: defunct English manufacturer of motor vehicles",
    "Q16987647 - Steel Company of Wales 401 Vulcan, 402 Valiant, and 403 Victor: class of 3 modern British 0-6-0ST locomotives",
    "Q919738 - British Motor Corporation: Australian company"
]

[
    "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)"
]

[
    "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."
]

[
    "Q8663 - Korean War: war between North and South Korea, 1950–1953",
    "Q2487880 - Korean War Veterans Parkway: highway in New York",
    "Q708847 - Korean War Veterans Memorial: national memorial in Washington, D.C., United States",
    "Q59480206 - Korean War Collection (NAID 104214901): collection in the National Archives and Records Administration's holdings",
    "Q1749621 - Korean officer's helmet: Korean helmet with a metal cap and leather neck protection"
]

[
    "Q13371 - Harvard University: private university in Cambridge, Massachusetts",
    "Q1587900 - Harvard University Press: American university publishing house",
    "Q3783572 - Harvard Art Museums: art museums at Harvard University",
    "Q3232931 - Harvard Library: library system of the Harvard University",
    "Q59611233 - Harvard Index of Botanists: database of botanists"
]

[
    "Q781034 - Austin A50 Cambridge: car model"
]

[
    "Q7040992 - Nissan L engine: motor vehicle engine family"
]

[
    "Q338545 - Nissan Z-car: car model",
    "Q2575681 - Nissan S30: Japanese Sports Car"
]

[
    "Q6446295 - Tokyu Kogyo Kurogane: Japanese automaker",
    "Q2534026 - Kurogane: manga by Kei Toume",
    "Q10314808 - Kurogane: character in Tsubasa: Reservoir Chronicle",
    "Q556083 - Kurogane: Japanese manga series",
    "Q112891394 - Kurogane: Japanese family name (黒金, くろがね)"
]

[
    "Q3489002 - Datsun 510: car model",
    "Q1324018 - Nissan Bluebird: Series of automobiles manufactured by Nissan"
]

[
    "Q36008 - Mercedes-Benz: German automobile brand owned by Mercedes-Benz AG",
    "Q27530 - Mercedes-Benz Group: German automotive manufacturer",
    "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"
]

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, Austin Motor Company relations (1937–1960s): From 1934, Datsun began to buildAustin 7sunder license. This operation became the greatest success ofAustin's overseas licensing of its Seven and marked the beginning of Datsun's international success. In 1952, Nissan entered into a legal agreement with Austin,for Nissan to assemble 2,000 Austins from imported partially assembled sets and sell them in Japan under the Austin trademark. The agreement called for Nissan to make all Austin parts locally within three years, a goal Nissan met. Nissan produced and marketed Austins for seven years. The agreement also gave Nissan the rights to use Austin patents, which Nissan used in developing its own engines for itsDatsunline of cars. In 1953, British-built Austins were assembled and sold, but by 1955, theAustin A50– completely built by Nissan and featuring a new 1489 cc engine — was on the market in Japan. Nissan produced 20,855 Austins from 1953 to 1959. Nissan leveraged the Austin patents to further develop its own modern engine designs beyond what Austin'sA-andB-familydesigns offered. The apex of the Austin-derived engines was the new designA series enginein 1966. In 1967, Nissan introduced its new highly advanced four-cylinder overhead cam (OHC)Nissan L engine, which while similar toMercedes-BenzOHC designs was a totally new engine designed by Nissan. This engine powered the newDatsun 510, which gained Nissan respect in the worldwidesedanmarket. Then, in 1969, Nissan introduced theDatsun 240Zsports car which used a six-cylinder variation of theL seriesengine, developed under Nissan Machinery (Nissan Koki Co., Ltd.日産工機) in 1964, a former remnant of another auto manufacturerKurogane. The240Zwas an immediate sensation and lifted Nissan to world-class status in the automobile market. During theKorean War, Nissan was a major vehicle producer for the US Army.After the Korean War ended, significant levels of anti-communist sentiment existed in Japan. The union that organized Nissan's workforce was strong and militant.Nissan was in financial difficulties, and when wage negotiations came, the company took a hard line. Workers were locked out, and several hundred were fired. The Japanese government and the US occupation forces arrested several union leaders.The union ran out of strike funds and was defeated. A new labor union was formed,with Shioji Ichiro one of its leaders. Ichiro had studied atHarvard Universityon a US government scholarship. He advanced an idea to trade wage cuts against saving 2,000 jobs.Ichiro's idea was made part of a new union contractthat prioritized productivity. Between 1955 and 1973, Nissan "expanded rapidly on the basis of technical advances supported – and often suggested – by the union." Ichiro became president of theConfederation of Japan Automobile Workers' Unionsand "the most influential figure in the right wing of the Japanese labor movement."

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)

P162 - producer: person(s) who produced the film, musical work, theatrical production, etc. (for film, this does not include executive producers, associate producers, etc.) [for production company, use P272, video games - use P178]

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

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

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

Q2575681 - Nissan S30: Japanese Sports Car

Datsun_240Z

P408 - software engine: software engine employed by the subject item

P516 - powered by: equipment or engine used by the subject to convert a source or energy into mechanical energy

P10271 - Engineer's Line Reference: code for a railway line in Britain owned by Network Rail

P631 - structural engineer: person, group or organisation responsible for the structural engineering of a building or structure

P1002 - engine configuration: configuration of an engine's cylinders

Q7040992 - Nissan L engine: motor vehicle engine family

Nissan_L_engine

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

Q27530 - Mercedes-Benz Group: German automotive manufacturer

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_L_engine

Q41156912 - Nisan: male given name (ניסן)

Nissan

Q1324018 - Nissan Bluebird: Series of automobiles manufactured by Nissan

Nissan

Nissan

Q6459088 - LMS Class 7F 0-8-0: class of 175 two-cylinder 0-8-0 locomotives

Nissan

Q110644484 - Austins: department store in Newton Abbot

Q105193002 - AustinShow: American Twitch streamer

Q4823355 - Austins: former department store in Derry

Q26548560 - 1 Courtenay Street: retail building in Newton Abbot, Teignbridge, Devon, England, UK

Q126699497 - Austins: retail building in Derry, County Londonderry, Northern Ireland

Nissan

Q510849 - Aston Martin V8: type of grand tourer manufactured by Aston Martin

Q14545571 - Aston Martin V8 (1996): full-size luxury car

Q750044 - Aston Martin V8 Zagato: sports car

Q43452497 - Aston Martin Vantage: Entry Level Two Seater Grand Touring Car Developed And Manufactured By British Car Manufacturer Aston Martin

Q2020659 - Aston Martin V8 Vantage: motor vehicle

Nissan

Q750017 - Aston Martin Rapide: car model

Q16834838 - Aston Martin Rapide Bertone Jet 2+2: one-off motor vehicle

Q50397157 - Aston Martin Rapide Concept: None

Q56321238 - Aston Martin Rapid E: Electric sports car

Nissan

Q750039 - Aston Martin Vantage: series of hand-built sports cars

Q750050 - Aston Martin Vantage: car model

Q131154543 - Aston Martin Vantage (2024): None

Q107566254 - Aston Martin Vantage AMR GT3: None

Nissan

Q113308284 - Aston Martin Zagato Centennial: One-off line of exotic car

Q2285877 - Aston Martin DB9 Volante: car model

Nissan

Q750056 - Aston Martin Virage: car model

Q606096 - Aston Martin Virage (2011): car model

Nissan

Nissan

Nissan

Q55442085 - Aston Martin DBS Superleggera: car model

Q2247109 - Aston Martin DBS Volante: None

Q65126493 - Aston Martin DBS GT Zagato: car model

Nissan

Q4811004 - Aston Martin Vantage GT2: grand touring race car

Q23900711 - Aston Martin Vantage GTE: endurance racing car

Nissan

Nissan

Q104620704 - Aston Martin F1 Team: Formula One racing team

Q130369454 - Aston Martin F1 Team (2021): British Formula One motor racing team

Q2704273 - David Brown Corporation: Formula One team powered by Aston Martin in1959

Q25412511 - Aston Martin in Formula One: Formula One activities of Aston Martin

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Q65043854 - Aston Martin Vantage GTE: endurance Grand Tourer developed by Aston Martin Racing

Q2359759 - Aston Martin Vantage GT4: racing car model

Nissan

Nissan

Nissan

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

Merger with Prince Motor Company

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, Merger with Prince Motor Company: In 1966, Nissan merged with thePrince Motor Company, bringing more upmarket cars, including theSkylineandGloria, into its selection. The Prince name was eventually abandoned, and successive Skylines and Glorias bore the Nissan name. "Prince" was used at the Japanese Nissan dealership "Nissan Prince Shop" until 1999, when "Nissan Red Stage" replaced it. Nissan Red Stage itself has been replaced as of 2007. The Skyline lives on as the G Series ofInfiniti. To capitalize on the renewed investment during1964 Summer Olympics, Nissan established the gallery on the second and third floors of the San-ai building, located inGinza, Tokyo. To attract visitors, Nissan started using beautiful female showroom attendants where Nissan held a competition to choose five candidates as the first class of Nissan Miss Fairladys, modeled after "Datsun Demonstrators" from the 1930s who introduced cars. The Fairlady name was used as a link to the popular Broadway playMy Fair Ladyof the era. Miss Fairladys became the marketers of theDatsun Fairlady 1500. In April 2008, 14 more Miss Fairlady candidates were added, for a total of 45 Nissan Miss Fairlady pageants (22 in Ginza, 8 in Sapporo, 7 in Nagoya, 7 in Fukuoka). In April 2012, 7 more Miss Fairlady candidates were added, for a total of 48 Nissan Miss Fairlady pageants (26 in Ginza, 8 in Sapporo, 7 in Nagoya, 7 in Fukuoka). In April 2013, 6 more Miss Fairlady candidates were added to Ginza showroom, for a total of 27 48th Ginza Nissan Miss Fairlady pageants.

[
    "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)"
]

[
    "Q2110378 - Prince Motor Company: company"
]

[
    "Q2649 - 1966: year",
    "Q19241629 - 1966: natural number",
    "Q134202 - 1966 FIFA World Cup: 8th FIFA World Cup, held in England",
    "Q918430 - 1966 United Kingdom general election: general election held in the United Kingdom",
    "Q53385098 - 1966-1967 one-year-period: period of about one year starting in 1966 and ending in 1967"
]

[
    "Q11649966 - Ginza: None",
    "Q746052 - Ginza: district of Chūō-ku, Tokyo",
    "Q133882896 - GiNZA: One of the Python libraries",
    "Q1940534 - Ginza Rabba: central religious text of Mandaeism",
    "Q3661964 - ginza: a regulatory commission or guild, overseers of silver production in Edo period Japan"
]

[
    "Q8420 - 1964 Summer Olympics: Games of the XVIII Olympiad, in Tokyo, Japan",
    "Q3628496 - athletics at the 1964 Summer Olympics – men's marathon: men's marathon events at the Olympics",
    "Q679541 - 1964 Summer Olympics medal table: None",
    "Q11524092 - 1964 Summer Olympics opening ceremony: None",
    "Q11524091 - 1964 Summer Olympics closing ceremony: closing ceremony of the Tokyo 2020 Summer Olympics"
]

[
    "Q7542 - Prince: American musician (1958–2016)",
    "Q16881414 - Prince: family name",
    "Q16280695 - Prince: male given name",
    "Q2747456 - prince: son of a prince, king, queen, emperor or empress, or other high-ranking person (such as a grand duke)",
    "Q241381 - HMS Prince Royal: 1610 55-gun royal ship of the line"
]

[
    "Q201215 - My Fair Lady: 1964 film by George Cukor",
    "Q591943 - My Fair Lady: 1956 musical based on Shaw's \"Pygmalion\"",
    "Q493774 - My Fair Lady: South Korean television drama",
    "Q484506 - My Fair Lady: 2009 South Korean television series",
    "Q6945347 - My Fair Lady: 1956 original Broadway cast recording"
]

[
    "Q11649966 - Ginza: None",
    "Q746052 - Ginza: district of Chūō-ku, Tokyo",
    "Q133882896 - GiNZA: One of the Python libraries",
    "Q1940534 - Ginza Rabba: central religious text of Mandaeism",
    "Q3661964 - ginza: a regulatory commission or guild, overseers of silver production in Edo period Japan"
]

[
    "Q627861 - 45: natural number",
    "Q6128115 - 7″ single: vinyl single; type of physical music format",
    "Q87533390 - ㊺: Unicode character",
    "Q1087 - rhodium: chemical element with symbol Rh and atomic number 45",
    "Q30313 - 45: year"
]

[
    "Q712764 - 48: natural number",
    "Q87533397 - ㊽: Unicode character",
    "Q1091 - cadmium: chemical element with symbol Cd and atomic number of 48",
    "Q28974562 - Brachybacterium faecium DSM 4810: bacterial strain",
    "Q30957 - 48: year"
]

[
    "Q37951 - Sapporo: city on Hokkaido, Japan",
    "Q9646 - 1972 Winter Olympics: 11th edition of Winter Olympics, in Sapporo, Japan",
    "Q801404 - Sapporo Station: railway station in Sapporo, Hokkaido, Japan",
    "Q931031 - 3473 Sapporo: asteroid",
    "Q27713365 - Sapporo igaku zasshi. The Sapporo medical journal: journal"
]

[
    "Q11751 - Nagoya: capital city of Aichi prefecture, Japan",
    "Q209961 - Chunichi Dragons: professional baseball team in the Central League of Nippon Professional Baseball",
    "Q1191132 - Nagoya University: national university of Japan",
    "Q49381 - Nagoya Station: railway station and metro station in Nakamura-ku, Nagoya, Aichi prefecture, Japan",
    "Q26842153 - Nagoya journal of medical science: journal"
]

[
    "Q16870048 - Fukuoka: family name",
    "Q26600 - Fukuoka: capital city of Fukuoka prefecture, Japan",
    "Q123258 - Fukuoka Prefecture: prefecture of Japan",
    "Q27710936 - Hukuoka Acta Medica: scientific journal",
    "Q11592406 - Fukuoka Nichinichi Shimbun: newspaper"
]

[
    "Q433353 - 27: natural number",
    "Q87533192 - ㉗: Unicode character",
    "Q740 - cobalt: chemical element with symbol Co and atomic number 27",
    "Q2360 - 27: year",
    "Q2822 - August 27: day of the year"
]

[
    "Q19617776 - April 1, 2008: Tuesday in April 2008",
    "Q17952985 - 2008-04-01: Di Luca może stracić maglia rosa: Wikinews article",
    "Q17953009 - 2008-04-01: Żart: Warszawa: jadowity pająk terroryzuje mieszkańców Pragi: Wikinews article",
    "Q17952996 - 2008-04-01: Żart: Czy na Wikipedii pojawią się reklamy?: Wikinews article",
    "Q17953002 - 2008-04-01: Żart: Hakerzy włamali się na stronę główną Wikipedii: Wikinews article"
]

[
    "Q17981964 - April 1, 2012: date",
    "Q131610106 - 2012-04-01 Dynamo Kyiv vs Chornomorets Odesa: association football match at NSC Olimpiyskyi during 2011–12 Ukrainian Premier League with score 3:1"
]


Nissan

Merger with Prince Motor Company

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, Merger with Prince Motor Company: In 1966, Nissan merged with thePrince Motor Company, bringing more upmarket cars, including theSkylineandGloria, into its selection. The Prince name was eventually abandoned, and successive Skylines and Glorias bore the Nissan name. "Prince" was used at the Japanese Nissan dealership "Nissan Prince Shop" until 1999, when "Nissan Red Stage" replaced it. Nissan Red Stage itself has been replaced as of 2007. The Skyline lives on as the G Series ofInfiniti. To capitalize on the renewed investment during1964 Summer Olympics, Nissan established the gallery on the second and third floors of the San-ai building, located inGinza, Tokyo. To attract visitors, Nissan started using beautiful female showroom attendants where Nissan held a competition to choose five candidates as the first class of Nissan Miss Fairladys, modeled after "Datsun Demonstrators" from the 1930s who introduced cars. The Fairlady name was used as a link to the popular Broadway playMy Fair Ladyof the era. Miss Fairladys became the marketers of theDatsun Fairlady 1500. In April 2008, 14 more Miss Fairlady candidates were added, for a total of 45 Nissan Miss Fairlady pageants (22 in Ginza, 8 in Sapporo, 7 in Nagoya, 7 in Fukuoka). In April 2012, 7 more Miss Fairlady candidates were added, for a total of 48 Nissan Miss Fairlady pageants (26 in Ginza, 8 in Sapporo, 7 in Nagoya, 7 in Fukuoka). In April 2013, 6 more Miss Fairlady candidates were added to Ginza showroom, for a total of 27 48th Ginza Nissan Miss Fairlady pageants.

Nissan

P7888 - merged into: the subject was dissolved and merged into the object, that existed previously

P11812 - afterward owned by: person or institution that owned the subject after stated event. Meant to be used as qualifier of “significant event (P793)” for events like sales, auctions, gifts, etc

P11811 - beforehand owned by: person or institution that owned the subject just before stated event. Meant to be used as qualifier of "significant event (P793)" for events like sales, auctions, gifts, etc

Q2110378 - Prince Motor Company: company

Prince_Motor_Company

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

Q43261 - horizon: apparent line that separates earth from sky

Q135075090 - Skyline: 2025 video game developed by Baknight

Q465837 - skyline: shape of a city's buildings against the sky

Q7537906 - Skyline High School: secondary school in Dallas, Texas, United States

Q1340087 - Skyline: 2010 film by Greg and Colin Strause

Prince_Motor_Company

Nissan

Nissan

Nissan

Nissan

Q643635 - Nissan Skyline: car model from Nissan

Q4999061 - Infiniti Q50: Compact executive car

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

Nissan

Q7542 - Prince: American musician (1958–2016)

Q2747456 - prince: son of a prince, king, queen, emperor or empress, or other high-ranking person (such as a grand duke)

Q241381 - HMS Prince Royal: 1610 55-gun royal ship of the line

Nissan

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

Q8420 - 1964 Summer Olympics: Games of the XVIII Olympiad, in Tokyo, Japan

Q3628496 - athletics at the 1964 Summer Olympics – men's marathon: men's marathon events at the Olympics

Q679541 - 1964 Summer Olympics medal table: None

Q11524092 - 1964 Summer Olympics opening ceremony: None

Q11524091 - 1964 Summer Olympics closing ceremony: closing ceremony of the Tokyo 2020 Summer Olympics

Nissan

Q746052 - Ginza: district of Chūō-ku, Tokyo

Q133882896 - GiNZA: One of the Python libraries

Q1940534 - Ginza Rabba: central religious text of Mandaeism

Q3661964 - ginza: a regulatory commission or guild, overseers of silver production in Edo period Japan

Nissan

Nissan

Q201215 - My Fair Lady: 1964 film by George Cukor

Q591943 - My Fair Lady: 1956 musical based on Shaw's "Pygmalion"

Q493774 - My Fair Lady: South Korean television drama

Q484506 - My Fair Lady: 2009 South Korean television series

Q6945347 - My Fair Lady: 1956 original Broadway cast recording

Nissan

Nissan

Nissan

Nissan

Output: None of the candidates

Output: None of the candidates


Nissan

Expansion into foreign markets

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, Expansion into foreign markets: In the 1950s Nissan decided to expand into worldwide markets. Nissan management realized, that their Datsun small car line would fulfill an unmet need in markets such as Australia and the world's largest car market, the United States. Nissan first showcased theDatsun Bluebirdat the 1958Los Angeles Auto Show.Nissan had entered the Middle East market in 1957 when it sold its first car in Saudi Arabia.The company formed a US subsidiary,Nissan Motor Corporation U.S.A., in Gardena, Californiain 1960 headed byYutaka Katayama.Nissan continued to improve the Sedan series with the latest technological advancements and chic Italianate styling for sporty cars such as theDatsun Fairladyroadsters, the race-winning411 series, theDatsun 510and theDatsun 240Z. By 1970 Nissan had become one of the world's largest exporters of automobiles.In 1970, Teocar was established, which was a Greek assembly plant created in cooperation with local Nissan distributor Theocharakis. Nissan Mexicana was established in the early-1960s and commenced manufacturing in 1966 at theCuernavaca assembly facility, making it Nissan's first North American assembly plant. In the wake of the1973 oil crisis, consumers worldwide, especially in the lucrative US market, began turning to high-quality small economy cars. To meet the growing demand the newNissan Sunny, the company built new factories in Mexico, Australia, New Zealand, Taiwan, United States, and South Africa. The "Chicken Tax" of 1964 placed a 25 percent tax on commercial vans imported to the United States. Nissan Motor Manufacturing Corporation USA was established in 1980.Nissan,Toyota Motor Corp., andHonda Motor Co.began establishing manufacturing plants in the US in the early-1980s.Nissan's initial assembly plantSmyrna assembly plantbroke ground in 1980 and at first only built trucks such as theDatsun 720and theNissan Hardbody Truck, but has since expanded to produce several car and SUV lines, including theNissan Altima, theNissan Maxima, as well as the Rogue, Pathfinder, Infiniti QX60, and theNissan Leafall-electric car. The addition of mass-market automobiles was in response to the 1981 Voluntary Export Restraints imposed by the US Government. An engine plant inDecherd, Tennesseefollowed. Most recently a second assembly plant was established inCanton, Mississippi. By the early-1980s, Nissan Datsun had long been the best selling Japanese brand in Europe.The Greek manufacturing plant itself was closed. A joint venture with Italy's then state-ownedAlfa Romeowas also entered in 1980, leading to Italian production of theNissan Cherryand an Alfa-badged and motorized version, theAlfa Romeo Arna. In 2001 Nissan established a manufacturing plant in Brazil. In 2005 Nissan added operations in India, through its subsidiaryNissan Motor India Private Limited.With its global alliance partnerRenault, Nissan invested $990 million to set up a manufacturing facility inChennai, catering to the Indian market as well as a base for exports of small cars to Europe. Nissan sold nearly 520,000 new vehicles in China in 2009 in a joint venture withDongfeng Motor. To meet increased production targets, Dongfeng-Nissan expanded its production base inGuangzhou, which would become Nissan's largest factory around the globe in terms of production capacity.Nissan also has moved and expanded its Nissan Americas Inc. headquarters, moving from Los Angeles to Franklin, Tennessee in the Nashville area. In the United States, Nissan has been increasing its reliance on sales to daily-rental companies likeEnterprise Rent-A-CarorHertz. In 2016, Nissan's rental sales jumped 37% and in 2017 Nissan became the only major automaker to boost rental sales when theDetroit Threecut back less profitable deliveries to daily-rental companies, which traditionally are the biggest customers of domestic automakers.

[
    "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."
]

[
    "Q846409 - Gardena: city located in the South Bay region of Los Angeles County, California, United States"
]

[
    "Q996433 - Smyrna: town in Rutherford County, Tennessee, United States",
    "Q133011342 - Smyrna, Tennessee: A Guide to the Town's History and Teaching it in Local Schools: online exhibition from Middle Tennessee State University"
]

[
    "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"
]

[
    "Q1476157 - Nissan Cherry: car model",
    "Q26847018 - Category:Nissan Cherry: Wikimedia category"
]

[
    "Q1541700 - Alfa Romeo Arna: car model"
]

[
    "Q7041008 - Nissan Motor India Private Limited: Indian subsidiary company"
]

[
    "Q155 - Brazil: country in South America",
    "Q16860380 - Brazil: family name",
    "Q750553 - Brazilian Portuguese: set of varieties of the Portuguese language spoken in Brazil",
    "Q96336868 - Brazil: vocal track by Frank Sinatra; 1957 studio recording; cover version",
    "Q96336800 - Brazil: vocal track by The Ritchie Family; 1975 studio recording; cover version"
]

[
    "Q1138452 - Dongfeng Motor Corporation: Chinese state-owned automobile manufacturer",
    "Q24839210 - Dongfeng Motor Corporation Station: Metro station in Wuhan, China",
    "Q5295889 - Dongfeng Motor Company Limited: Chinese automobile manufacturer",
    "Q11104175 - Dongfeng Motor Group Company Limited: Chinese automobile manufacturer",
    "Q10871167 - Dongfeng DMU: first diesel multiple unit built in China"
]

[
    "Q984264 - Franklin: city and county seat of Williamson County, Tennessee, United States"
]

[
    "Q17085454 - Enterprise Rent-A-Car: American car and truck rental company",
    "Q131883691 - Enterprise Rent-A-Car UK Limited: None"
]

[
    "Q1371559 - Hertz: family name",
    "Q39369 - hertz: SI unit of frequency",
    "Q135027733 - Hertz: 2024 video game developed by Arcaludi",
    "Q108533712 - Hertz: None",
    "Q21450384 - Hertzberg: family name"
]

[
    "Q911323 - Nissan Leaf: compact electric hatchback manufactured by Nissan",
    "Q38278176 - Nissan Leaf (first generation): battery electric compact car",
    "Q50398548 - Nissan Leaf Nismo Concept: None",
    "Q50398550 - Nissan Leaf Nismo Concept: None"
]

[
    "Q1993846 - Nissan Rogue: automobile",
    "Q30896335 - Nissan Rogue Warrior Project: None"
]

[
    "Q377287 - Nissan Maxima: car model",
    "Q1675830 - Nissan Cima: motor vehicle"
]

[
    "Q1132451 - Nissan Sunny: car model",
    "Q719974 - Nissan Sentra: car model from Nissan",
    "Q11509844 - Nissan Latio: Subcompact car model from Nissan",
    "Q11509831 - Nissan Sunny California: None",
    "Q28721699 - Nissan Sunny: None"
]

[
    "Q1419007 - Nissan Pathfinder: SUV model from Nissan",
    "Q452035 - Nissan Terrano: SUV model from Nissan",
    "Q50397886 - Nissan Pathfinder Concept: None"
]

[
    "Q929618 - Nissan Hardbody Truck: Pickup truck model from Nissan"
]

[
    "Q27543 - Datsun: automobile brand of Nissan"
]

[
    "Q26261942 - Nissan Versa: Subcompact car model from Nissan",
    "Q11509844 - Nissan Latio: Subcompact car model from Nissan",
    "Q116922573 - Nissan versa: None",
    "Q11509846 - Nissan Note (E12): Second-generation model of the Nissan Note"
]

[
    "Q26186522 - Nissan Kicks: subcompact crossover SUV"
]

[
    "Q257673 - Nissan Atlas: car model",
    "Q109927063 - Nissan Atlas I: Nissan's light and medium-duty truck",
    "Q1674936 - Nissan Atlas Max: None",
    "Q109927154 - Nissan Atlas II: Nissan's light and medium-duty truck"
]

[
    "Q87209760 - Nissan Z: seventh generation of the Nissan Z sports car",
    "Q269036 - Nissan 350Z: top sports car",
    "Q338545 - Nissan Z-car: car model",
    "Q2575681 - Nissan S30: Japanese Sports Car",
    "Q13218302 - Nissan 300ZX: Nissan Motor car model"
]

[
    "Q87209760 - Nissan Z: seventh generation of the Nissan Z sports car",
    "Q269036 - Nissan 350Z: top sports car",
    "Q2575681 - Nissan S30: Japanese Sports Car",
    "Q338545 - Nissan Z-car: car model",
    "Q13218302 - Nissan 300ZX: Nissan Motor car model"
]

[
    "Q1135638 - Nissan X-Trail: automobile model series",
    "Q126424490 - Nissan X-Trail I: automobile model",
    "Q11884573 - x trail crdi: None"
]

[
    "Q1419007 - Nissan Pathfinder: SUV model from Nissan",
    "Q452035 - Nissan Terrano: SUV model from Nissan",
    "Q523437 - Nissan Terrano II: Compact SUV"
]

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

[
    "Q1132451 - Nissan Sunny: car model",
    "Q719974 - Nissan Sentra: car model from Nissan",
    "Q18819706 - Nissan Sentra B16: None",
    "Q108537486 - Nissan Sentra B17: None"
]

[
    "Q936668 - Nissan 370Z: car model"
]

[
    "Q1993801 - Nissan Pulsar: Japanese small car",
    "Q719974 - Nissan Sentra: car model from Nissan",
    "Q18432709 - Nissan Pulsar (Europe): None",
    "Q1569101 - Nissan Pulsar GTI-R: Hot hatch model from Nissan",
    "Q1141759 - Nissan Almera: Automobile By Nissan"
]

[
    "Q375074 - Nissan Violet: car model",
    "Q1993858 - Nissan Stanza: None",
    "Q3702978 - Nissan Violet 160J: None"
]

[
    "Q1366053 - Nissan Fuga: car model",
    "Q1208796 - Mitsubishi Proudia: luxury car model from Mitsubishi Motors",
    "Q11509852 - Nissan Fuga: None",
    "Q1662408 - Infiniti M: Type of luxury car"
]

[
    "Q11509844 - Nissan Latio: Subcompact car model from Nissan",
    "Q26261942 - Nissan Versa: Subcompact car model from Nissan"
]

[
    "Q1278086 - Nissan Murano: car model",
    "Q131822738 - Nissan Murano (Z53): fourth generation of the Nissan Murano",
    "Q11235694 - NISSAN MURANO CROSSOVER STYLE: None"
]

[
    "Q672060 - Nissan Cefiro: car model",
    "Q11509842 - Nissan Cefiro Wagon: None"
]

[
    "Q87209760 - Nissan Z: seventh generation of the Nissan Z sports car",
    "Q5930093 - Nissan Z engine: motor vehicle engine",
    "Q338545 - Nissan Z-car: car model",
    "Q7041090 - Nissan ZD engine: motor vehicle engine",
    "Q109928056 - Nissan ZD30: straight-4 naturally aspirated diesel engine manufactured by Nissan"
]

[
    "Q15181278 - Nissan ZEOD RC: hybrid electric racing car"
]

[
    "Q132125644 - Nissan Altima: song by Doechii",
    "Q636791 - Nissan Altima: car model from Nissan",
    "Q135398086 - Nissan Altima: 2024 single by Doechii",
    "Q135398076 - Nissan Altima: vocal track by Doechii"
]

[
    "Q1820396 - Nissan Titan: Fullsize pickup truck"
]

[
    "Q1809938 - Nissan Xterra: SUV model from Nissan"
]

[
    "Q1063020 - Nissan 180SX: Japanese Sports Car",
    "Q1480095 - Nissan Silvia: car model"
]

[
    "Q1584101 - Nissan Presage: car model"
]

[
    "Q643635 - Nissan Skyline: car model from Nissan",
    "Q4999061 - Infiniti Q50: Compact executive car",
    "Q11509834 - Nissan Skyline: None",
    "Q11509836 - Nissan Skyline: None",
    "Q3342164 - Nissan Skyline (R34): Tenth-generation model of the Nissan Skyline"
]

[
    "Q269036 - Nissan 350Z: top sports car"
]

Output: None of the candidates

Output: None of the candidates


Nissan

Expansion into foreign markets

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, Expansion into foreign markets: In the 1950s Nissan decided to expand into worldwide markets. Nissan management realized, that their Datsun small car line would fulfill an unmet need in markets such as Australia and the world's largest car market, the United States. Nissan first showcased theDatsun Bluebirdat the 1958Los Angeles Auto Show.Nissan had entered the Middle East market in 1957 when it sold its first car in Saudi Arabia.The company formed a US subsidiary,Nissan Motor Corporation U.S.A., in Gardena, Californiain 1960 headed byYutaka Katayama.Nissan continued to improve the Sedan series with the latest technological advancements and chic Italianate styling for sporty cars such as theDatsun Fairladyroadsters, the race-winning411 series, theDatsun 510and theDatsun 240Z. By 1970 Nissan had become one of the world's largest exporters of automobiles.In 1970, Teocar was established, which was a Greek assembly plant created in cooperation with local Nissan distributor Theocharakis. Nissan Mexicana was established in the early-1960s and commenced manufacturing in 1966 at theCuernavaca assembly facility, making it Nissan's first North American assembly plant. In the wake of the1973 oil crisis, consumers worldwide, especially in the lucrative US market, began turning to high-quality small economy cars. To meet the growing demand the newNissan Sunny, the company built new factories in Mexico, Australia, New Zealand, Taiwan, United States, and South Africa. The "Chicken Tax" of 1964 placed a 25 percent tax on commercial vans imported to the United States. Nissan Motor Manufacturing Corporation USA was established in 1980.Nissan,Toyota Motor Corp., andHonda Motor Co.began establishing manufacturing plants in the US in the early-1980s.Nissan's initial assembly plantSmyrna assembly plantbroke ground in 1980 and at first only built trucks such as theDatsun 720and theNissan Hardbody Truck, but has since expanded to produce several car and SUV lines, including theNissan Altima, theNissan Maxima, as well as the Rogue, Pathfinder, Infiniti QX60, and theNissan Leafall-electric car. The addition of mass-market automobiles was in response to the 1981 Voluntary Export Restraints imposed by the US Government. An engine plant inDecherd, Tennesseefollowed. Most recently a second assembly plant was established inCanton, Mississippi. By the early-1980s, Nissan Datsun had long been the best selling Japanese brand in Europe.The Greek manufacturing plant itself was closed. A joint venture with Italy's then state-ownedAlfa Romeowas also entered in 1980, leading to Italian production of theNissan Cherryand an Alfa-badged and motorized version, theAlfa Romeo Arna. In 2001 Nissan established a manufacturing plant in Brazil. In 2005 Nissan added operations in India, through its subsidiaryNissan Motor India Private Limited.With its global alliance partnerRenault, Nissan invested $990 million to set up a manufacturing facility inChennai, catering to the Indian market as well as a base for exports of small cars to Europe. Nissan sold nearly 520,000 new vehicles in China in 2009 in a joint venture withDongfeng Motor. To meet increased production targets, Dongfeng-Nissan expanded its production base inGuangzhou, which would become Nissan's largest factory around the globe in terms of production capacity.Nissan also has moved and expanded its Nissan Americas Inc. headquarters, moving from Los Angeles to Franklin, Tennessee in the Nashville area. In the United States, Nissan has been increasing its reliance on sales to daily-rental companies likeEnterprise Rent-A-CarorHertz. In 2016, Nissan's rental sales jumped 37% and in 2017 Nissan became the only major automaker to boost rental sales when theDetroit Threecut back less profitable deliveries to daily-rental companies, which traditionally are the biggest customers of domestic automakers.

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

Nissan

Q911323 - Nissan Leaf: compact electric hatchback manufactured by Nissan

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

Q50398548 - Nissan Leaf Nismo Concept: None

Q50398550 - Nissan Leaf Nismo Concept: None

Nissan

Q30896335 - Nissan Rogue Warrior Project: None

Nissan

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

Nissan

Q1419007 - Nissan Pathfinder: SUV model from Nissan

Q452035 - Nissan Terrano: SUV model from Nissan

Q50397886 - Nissan Pathfinder Concept: None

Nissan

Nissan

Q719974 - Nissan Sentra: car model from Nissan

Q11509844 - Nissan Latio: Subcompact car model from Nissan

Q11509831 - Nissan Sunny California: None

Nissan

Q929618 - Nissan Hardbody Truck: Pickup truck model from Nissan

Nissan

Nissan

Q26847018 - Category:Nissan Cherry: Wikimedia category

Nissan

Nissan

Nissan

Q1135638 - Nissan X-Trail: automobile model series

Q126424490 - Nissan X-Trail I: automobile model

Nissan

Q926124 - Nissan Qashqai: compact crossover SUV by Nissan

Q116256985 - Nissan Qashqai (J10): motor vehicle

Q50397087 - Nissan Qashqai Concept: None

Nissan

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

Q5930093 - Nissan Z engine: motor vehicle engine

Q7041090 - Nissan ZD engine: motor vehicle engine

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

Nissan

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

Nissan

Q523437 - Nissan Terrano II: Compact SUV

Nissan

Q1993801 - Nissan Pulsar: Japanese small car

Q18432709 - Nissan Pulsar (Europe): None

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

Q1141759 - Nissan Almera: Automobile By Nissan

Nissan

Q26261942 - Nissan Versa: Subcompact car model from Nissan

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

Nissan

Q26186522 - Nissan Kicks: subcompact crossover SUV

Nissan

Q2575681 - Nissan S30: Japanese Sports Car

Q13218302 - Nissan 300ZX: Nissan Motor car model

Nissan

Nissan

Q15181278 - Nissan ZEOD RC: hybrid electric racing car

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Q1208796 - Mitsubishi Proudia: luxury car model from Mitsubishi Motors

Q1662408 - Infiniti M: Type of luxury car

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Q422004 - Nissan Sylphy: car model by Nissan

Nissan

Nissan

Nissan

Nissan

Q1063020 - Nissan 180SX: Japanese Sports Car

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Q132125644 - Nissan Altima: song by Doechii

Q636791 - Nissan Altima: car model from Nissan

Q135398086 - Nissan Altima: 2024 single by Doechii

Q135398076 - Nissan Altima: vocal track by Doechii

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Q672060 - Nissan Cefiro: car model
Q11509842 - Nissan Cefiro Wagon: None

Nissan

Q121769546 - Nissan Cube (третье поколение): None

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


Nissan

Project 901

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, Project 901: Project 901 was an initiative in the mid-1980s, and it represented a desire for Nissan to offer technologically advanced cars by 1990. Known by various names such as P901 Activity, 901 Activity, 901 Plan, 901 Operation, and Project 901, the project began under the leadership of Nissan President Yutaka Kume (1985–1992). Project 901 was initiated in early 1985 in response to the growing demand for new, technically advanced cars from all Japanese manufacturers. With a market share of around 25% then, Nissan saw a decline to less than 20%, prompting the company to address its shortcomings. Under the banner of 'Aiming to be the world's best in technology by the 1990s,' the plan focused on the technological development of chassis, engines, suspensions, handling, design, and quality improvements for all car models to be introduced before 1990.

[
    "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)"
]

[
    "Q17032105 - Yutaka Kume: Japanese businessperson (1921-2014)"
]

[
    "Q3975 - computer case: enclosure that contains most of the components of a computer",
    "Q11326220 - housing: exterior case or enclosure used to protect an interior mechanism (considered an integral part of the equipment and necessary for its operation, as opposed to mere packaging)",
    "Q1068107 - chassis: load-bearing framework of an artificial object, which structurally supports the object in its construction and function",
    "Q9189209 - Chassis: music band",
    "Q304948 - vehicle identification number: unique code including a serial number, used by the automotive industry to identify individual motor vehicles, towed vehicles, motorcycles, scooters and mopeds"
]

[
    "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",
    "Q11023 - engineering: applied science"
]

[
    "Q87406427 - account suspension: action performed by an administrative entity to deny an account from interacting with their website",
    "Q272870 - suspension: system of tires, tire air, springs, shock absorbers and linkages that connects a vehicle to its wheels and allows relative motion between the two",
    "Q26100 - suspension: heterogeneous mixture",
    "Q1188533 - suspension: time away from a workplace as ordered by the employer in order for a workplace investigation to take place, or time away from school as punishment",
    "Q12570 - suspension bridge: type of bridge"
]

[
    "Q620591 - apotropaic magic: type of magic intended to turn away harm or evil influences",
    "Q1413942 - material handling: sub-discipline of mechanical engineering concerned with the protection, storage, and control of materials throughout their manufacturing, warehousing, distribution, consumption, and disposal",
    "Q870020 - automobile handling: description of the way a wheeled vehicle responds and reacts to the inputs of a driver, as well as how it moves along a track or road",
    "Q83891752 - Handling: human settlement in Waidhofen an der Thaya",
    "Q96274187 - handling editor: editor in charge of organising a scholarly peer review"
]

[
    "Q82604 - design: creation of a plan or convention for the construction of an object or a system; process of creation; act of creativity and innovation",
    "Q47035253 - C: NRHP criterion - design/construction",
    "Q811979 - architectural structure: human-designed and -made structure",
    "Q1229071 - motif: in the visual arts, individual design element, alone or combined to produce a pattern",
    "Q5322166 - designer: person who designs"
]

[
    "Q1207505 - quality: distinguishing feature",
    "Q13100823 - quality of life: term for the quality of the various domains in human life and general well-being of individuals and societies",
    "Q185957 - quality: non-inferiority or superiority of something; a perceptual, conditional, and somewhat subjective attribute that may be understood differently by different people",
    "Q17559452 - recommended article: badge being used for recommended articles (also known as quality articles or usable articles)",
    "Q827792 - quality control: process or activity to ensure the quality of products and services provided to customers"
]


Nissan

Project 901

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, Project 901: Project 901 was an initiative in the mid-1980s, and it represented a desire for Nissan to offer technologically advanced cars by 1990. Known by various names such as P901 Activity, 901 Activity, 901 Plan, 901 Operation, and Project 901, the project began under the leadership of Nissan President Yutaka Kume (1985–1992). Project 901 was initiated in early 1985 in response to the growing demand for new, technically advanced cars from all Japanese manufacturers. With a market share of around 25% then, Nissan saw a decline to less than 20%, prompting the company to address its shortcomings. Under the banner of 'Aiming to be the world's best in technology by the 1990s,' the plan focused on the technological development of chassis, engines, suspensions, handling, design, and quality improvements for all car models to be introduced before 1990.

Nissan

Project_901

Q106637515 - 1985: vocal track by Patrik Isaksson; 2004 studio recording

Q110293939 - 1985: Belgian television series

Q106637553 - 1985: 2004 single by Patrik Isaksson

Project_901

P169 - chief executive officer: highest-ranking corporate officer appointed as the CEO within an organization

Q17032105 - Yutaka Kume: Japanese businessperson (1921-2014)

Nissan

Q69307427 - January 25, 2025: date in Gregorian calendar

Q69307486 - March 25, 2025: date in Gregorian calendar

Q69307517 - April 25, 2025: date in Gregorian calendar

Output: None of the candidates


Nissan

Alliance with Renault

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, Alliance with Renault: In 1999, facing severe financial difficulties, Nissan entered an alliance withRenaultof France.In June 2001, Renault executiveCarlos Ghosnwas named chief executive officer of Nissan. In May 2005, Ghosn was named president of Nissan's partner company Renault. He was appointed president and CEO of Renault on 6 May 2009. Under CEO Ghosn's "Nissan Revival Plan" (NRP), the company has rebounded in what many leading economists consider to be one of the most spectacular corporate turnarounds in history, catapulting Nissan to record profits and a dramatic revitalization of both its Nissan andInfinitimodel line-ups.Ghosn has been recognized in Japan for the company's turnaround in the midst of anailing Japanese economy. Ghosn and the Nissan turnaround were featured in Japanesemangaand popular culture. His achievements in revitalizing Nissan were noted by the Japanese government, which awarded him theJapan Medal with Blue Ribbonin 2004. On 1 April 2017, Ghosn stepped down as CEO of Nissan, while remaining chairman of the company. He was replaced as CEO by his then-deputyHiroto Saikawa.On 19 November 2018, Ghosn was fired as chairman following his arrest for the alleged under-reporting of his income to Japanese financial authorities.After 108 days in detention, Ghosn was released on bail, but after 29 days he was again detained on new charges (4 April 2019). He had been due to hold a news conference, but instead, his lawyers released a video of Ghosn alleging this 2018–19 Nissan scandal is itself evidence of value destruction and Nissan corporate mismanagement.In September 2019, Saikawa resigned as CEO, following allegations of improper payments received by him.Yasuhiro Yamauchi was appointed as acting CEO.In October 2019, the company appointed Makoto Uchida as its next CEO.On 1 December 2019, Uchida became CEO. In January 2023, Renault said it intended to transfer almost 30% of its controlling stake in Nissan to a French trust (pending approval by both companies), reducing its shares with voting rights to a minority 15% and, in doing so, matching Nissan shares in Renault to gain equal voting rights. The shareholding and voting ratio of both companies is set to be fixed in the future. The agreement also included Nissan investing in Ampere (a proposed Renault subsidiary for electric cars) and projects in various markets.In February 2023, both companies approved the going-ahead for the shareholding changes. Final details and regulatory clearances for the transaction were set to be completed by the first quarter of 2023 and it would be done by the fourth quarter. The companies also approved joint projects and Nissan's Ampere investment.The share transfer was completed in November 2023.

[
    "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)"
]

[
    "Q356719 - Carlos Ghosn: French-Brazilian-Lebanese businessman",
    "Q107618108 - Carlos Ghosn's Mad Dash Captivates Japan: \"His Escape Was Like a Novel\": None",
    "Q118457363 - Carlos Ghosn: The Last Flight: tv movie/episiode",
    "Q97600312 - Carlos Ghosn : le conseil d'administration de Renault refuse de payer ses indemnités de départ: None",
    "Q116202634 - Fugitive: The Curious Case Of Carlos Ghosn: 2022 film directed by Lucy Blakstad"
]

[
    "Q11509786 - Nissan Revival Plan: None"
]

[
    "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"
]

[
    "Q142 - France: country in Western Europe and other continents (through its overseas territories in America, Africa and Oceania)",
    "Q3080569 - France: unisex given name",
    "Q16275867 - France: family name",
    "Q47774 - France men's national association football team: national association football team representing France",
    "Q2268455 - Francesco: male given 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)"
]

[
    "Q29714 - Infiniti: Japanese luxury vehicle brand owned by Nissan",
    "Q179230 - infinitive: grammatical form",
    "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"
]

[
    "Q60487225 - 108.0 WMD KBOOM FM: fictional radio station from the 2006 video game Saints Row",
    "Q4546745 - 108.0 FM: Wikimedia list article",
    "Q20821277 - Eko FM: radio station in Lagos"
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[
    "Q54583800 - 29.0.C.27: cell line",
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    "Q69307431 - January 29, 2025: date in Gregorian calendar",
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    "Q69307521 - April 29, 2025: date in Gregorian calendar"
]

[
    "Q319831 - economy of Japan: economy of the country",
    "Q15756342 - Japanese Economy: journal",
    "Q67694488 - Japanese Economy (NAID 1664929): item in the National Archives and Records Administration's holdings",
    "Q67694382 - Japanese Economy (NAID 1663471): item in the National Archives and Records Administration's holdings",
    "Q118887249 - Japanese Economy: Its Present Status and Prospects: None"
]

[
    "Q89784273 - Makoto Uchida: Japanese particle physicist",
    "Q3489767 - Makoto Uchida: Japanese video game developer",
    "Q70092183 - Makoto Uchida: Japanese businessman",
    "Q86035976 - Makoto Uchida: researcher (ORCID 0000-0002-7847-3727)",
    "Q92739432 - Makoto Uchida: researcher (ORCID 0000-0002-5445-4580)"
]

[
    "Q30923926 - Hiroto Saikawa: Japanese businessman, former president of Nissan"
]

[
    "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)"
]

[
    "Q69306665 - January 1, 2023: date in Gregorian calendar"
]

[
    "Q69307000 - November 30, 2023: date in Gregorian calendar"
]

[
    "Q69306755 - April 1, 2023: date in Gregorian calendar"
]

[
    "Q69305319 - January 1, 2004: date in Gregorian calendar"
]

[
    "Q22663793 - May 6, 2009: Wednesday in May 2009",
    "Q17966834 - 2009-05-06: Bunt wojskowy w Gruzji: Wikinews article",
    "Q17966827 - 2009-05-06: Afganistan: w nalocie USA mogło zginąć 100 cywili: Wikinews article"
]

[
    "Q22663079 - May 1, 2005: Sunday in May 2005",
    "Q19768593 - 2005-05-01: Firefox: Pięćdziesiąt milionów pobrań: Wikinews article",
    "Q17876076 - 2005-05-01: Dzień Celtycki kończy Podkarpacki Jarmark Turystyczny: Wikinews article"
]

[
    "Q69302528 - January 1, 1999: date in Gregorian calendar"
]

[
    "Q69303868 - June 1, 2001: date in Gregorian calendar"
]

[
    "Q33122557 - April 1, 2017: Saturday in April 2017",
    "Q131607767 - 2017-04-01 Dynamo Kyiv U-19 vs Karpaty Lviv U-19: association football match at Dynamo Training Center in Koncha-Zaspa during 2016–17 Ukrainian Football Championship U-19 with score 2:1"
]

[
    "Q57316168 - January 1, 2019: Tuesday in January 2019"
]

[
    "Q57349778 - February 1, 2019: Friday in February 2019"
]

[
    "Q57350492 - September 1, 2019: Sunday in September 2019",
    "Q131605784 - 2019-09-01 Zorya Luhansk vs Dynamo Kyiv: association football match at Slavutych-Arena during 2019–20 Ukrainian Premier League with score 2:2"
]

[
    "Q59779657 - Yasuhiro Yamauchi: None",
    "Q40042566 - Yasuhiro Yamauchi: None"
]

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