Nissan

Nissan

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

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

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

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nissan, Nissan: Nissan Motor Co., Ltd.[b],doing business asNissanand formerlyJidosha-Seizo, is a Japanese multinationalautomobile manufacturerheadquartered inYokohama, Kanagawa, Japan. The company sells its vehicles under theNissanandInfinitibrands, and formerly theDatsunbrand, with in-house performance tuning products (including cars) under theNismoandAutechbrands. The company can be traced back to the beginning of the 20th century, with the Nissanzaibatsuor calledNissan Group. Since 1999, Nissan has been part of theRenault–Nissan–Mitsubishi Alliance(Mitsubishi joining in 2016), a partnership between Nissan andMitsubishi Motorsof Japan, withRenaultof France. As of November 2023[update], Renault holds a 15% voting stake in Nissan, while Nissan holds the same stake in Renault.Since October 2016, Nissan held a 34% controlling stake in Mitsubishi Motors.In November 2024, Nissan reduced its stake in Mitsubishi Motors from 34% to 24%. In 2017, Nissan was the sixth largest automaker in the world, afterToyota,Volkswagen Group,Hyundai Motor Group,General MotorsandFord.With a revenue of $78 billion in 2022, Nissan was the ninth largest automobile maker in the world. Nissan planned to merge withHonda Motor Companyin 2026, after an announcement in December 2024.However by February 2025, Nissan announced it would abandon merger plans as the automaker stated that it wanted to become an equal partner to Honda rather than a subsidiary.In November 2024, a Nissan executive was quoted as saying that the company had as little as 12 months left to live, barring any major events.As of 2025[update], Nissan is having major financial issues.

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Nissan

Nissan

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

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

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nissan, Nissan: Nissan Motor Co., Ltd.[b],doing business asNissanand formerlyJidosha-Seizo, is a Japanese multinationalautomobile manufacturerheadquartered inYokohama, Kanagawa, Japan. The company sells its vehicles under theNissanandInfinitibrands, and formerly theDatsunbrand, with in-house performance tuning products (including cars) under theNismoandAutechbrands. The company can be traced back to the beginning of the 20th century, with the Nissanzaibatsuor calledNissan Group. Since 1999, Nissan has been part of theRenault–Nissan–Mitsubishi Alliance(Mitsubishi joining in 2016), a partnership between Nissan andMitsubishi Motorsof Japan, withRenaultof France. As of November 2023[update], Renault holds a 15% voting stake in Nissan, while Nissan holds the same stake in Renault.Since October 2016, Nissan held a 34% controlling stake in Mitsubishi Motors.In November 2024, Nissan reduced its stake in Mitsubishi Motors from 34% to 24%. In 2017, Nissan was the sixth largest automaker in the world, afterToyota,Volkswagen Group,Hyundai Motor Group,General MotorsandFord.With a revenue of $78 billion in 2022, Nissan was the ninth largest automobile maker in the world. Nissan planned to merge withHonda Motor Companyin 2026, after an announcement in December 2024.However by February 2025, Nissan announced it would abandon merger plans as the automaker stated that it wanted to become an equal partner to Honda rather than a subsidiary.In November 2024, a Nissan executive was quoted as saying that the company had as little as 12 months left to live, barring any major events.As of 2025[update], Nissan is having major financial issues.

[["Nissan", "has_product_line", "Infiniti"], ["Nissan", "has_product_line", "Datsun"], ["Nissan", "has_product_line", "Nissan"], ["Nissan", "subsidiary", "Nissan_Motor_Co._Ltd."], ["Nissan", "parent_company", "Renault"], ["Nissan", "subsidiary", "Autech"], ["Nissan", "subsidiary", "Nismo"], ["Nissan", "founded_by", "Nissan_Zaibatsu"], ["Nissan", "has_product_line", "Mitsubishi_Motors"], ["Nissan", "shares", "15.0"], ["Nissan", "subsidiary", "Mitsubishi_Motors"], ["Nissan", "revenue", "78000000000"], ["Nissan", "rank", "6"], ["Nissan", "subsidiary", "Honda_Motor_Company"], ["Nissan", "shares", "24.0"], ["Nissan", "subsidiary", "Renault"], ["Nissan", "has_product_line", "Nissan_Motor"], ["Nissan", "subsidiary", "Fuji_Corporation"], ["Nissan", "has_product_line", "Abarth"], ["Nissan", "subsidiary", "Alfa_Romeo"], ["Nissan", "has_product_line", "AMC"], ["Nissan", "has_product_line", "Ferrari"], ["Nissan", "has_product_line", "Lamborghini"], ["Nissan", "has_product_line", "Maserati"], ["Nissan", "has_product_line", "Pagani"], ["Nissan", "has_product_line", "Renault"], ["Nissan", "has_product_line", "Saab"], ["Nissan", "has_product_line", "Smart"], ["Nissan", "has_product_line", "Suzuki"], ["Nissan", "has_product_line", "Volkswagen"], ["Nissan", "subsidiary", "Zenvo"], ["Nissan", "founded_by", "Masuhiro_Suzuki"], ["Nissan", "subsidiary", "Audi"], ["Nissan", "subsidiary", "BMW"], ["Nissan", "subsidiary", "Fiat"], ["Nissan", "subsidiary", "Peugeot"], ["Nissan", "subsidiary", "Citroën"], ["Nissan", "subsidiary", "Daimler"], ["Nissan", "subsidiary", "Mercedes-Benz"], ["Nissan", "subsidiary", "Opel"], ["Nissan", "subsidiary", "Pontiac"], ["Nissan", "subsidiary", "Lancia"], ["Nissan", "subsidiary", "Ferrari"], ["Nissan", "subsidiary", "Mitsubishi"], ["Nissan", "subsidiary", "Renault"], ["Nissan", "subsidiary", "Vauxhall"], ["Nissan", "subsidiary", "Aston_Martin"], ["Nissan", "subsidiary", "Jaguar"], ["Nissan", "subsidiary", "Rolls-Royce_(automobile_engineer)": "Rolls-Royce"], ["Nissan", "subsidiary", "Saab"], ["Nissan", "subsidiary", "AMC"], ["Nissan", "subsidiary", "Lincoln"], ["Nissan", "subsidiary", "Lotus_(automobile_devilopment_co.)"], ["Nissan", "subsidiary", "Bugatti"], ["Nissan", "subsidiary", "Fiat_Chrysler"], ["Nissan", "subsidiary", "Alfa_Romeo"], ["Nissan", "subsidiary", "Caterham"], ["Nissan", "subsidiary", "Dodge"], ["Nissan", "subsidiary", "Feng_Shou"], ["Nissan", "subsidiary", "GMC"], ["Nissan", "subsidiary", "Hino"], ["Nissan", "subsidiary", "Isuzu"], ["Nissan", "subsidiary", "Koenigsegg"], ["Nissan", "subsidiary", "Lamborghini"], ["Nissan", "subsidiary", "Maserati"], ["Nissan", "subsidiary", "McLaren"], ["Nissan", "subsidiary", "Pagani"], ["Nissan", "subsidiary", "Peugeot"], ["Nissan", "subsidiary", "Pontiac"], ["Nissan", "subsidiary", "Renault"], ["Nissan", "subsidiary", "Saab"], ["Nissan", "subsidiary", "Smart"], ["Nissan", "subsidiary", "Suzuki"], ["Nissan", "subsidiary", "Volkswagen"], ["Nissan", "subsidiary", "Abarth"], ["Nissan", "subsidiary", "Zenvo"], ["Nissan", "subsidiary", "Fuji_Corporation"], ["Nissan", "subsidiary", "Masuhiro_Suzuki"], ["Nissan", "subsidiary", "Honda_Motor_Company"], ["Nissan", "subsidiary", "Audi"], ["Nissan", "subsidiary", "BMW"], ["Nissan", "subsidiary", "Fiat"], ["Nissan", "subsidiary", "Citroën"], ["Nissan", "subsidiary", "Daimler"], ["Nissan", "subsidiary", "Mercedes-Benz"], ["Nissan", "subsidiary", "Opel"], ["Nissan", "subsidiary", "Aston_Martin"], ["Nissan", "subsidiary", "Jaguar"], ["Nissan", "subsidiary", "Rolls-Royce_(automobile_engineer)"], ["Nissan", "subsidiary", "Feng_Shou"], ["Nissan", "subsidiary", "GMC"], ["Nissan", "subsidiary", "Hino"], ["Nissan", "subsidiary", "Isuzu"], ["Nissan", "subsidiary", "Koenigsegg"], ["Nissan", "subsidiary", "Lancia"], ["Nissan", "subsidiary", "Mitsubishi"], ["Nissan", "subsidiary", "Lotus_(automobile_devilopment_co.)"], ["Nissan", "subsidiary", "Bugatti"], ["Nissan", "subsidiary", "Fiat_Chrysler"], ["Nissan", "subsidiary", "McLaren"], ["Nissan", "subsidiary", "Dodge"], ["Nissan", "subsidiary", "Caterham"], ["Nissan", "subsidiary", "Alfa_Romeo"}]


Nissan

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

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

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

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

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

[["Datsun", "Company"], ["Nissan", "Funding"], ["Datsun_Type_11", "Product"], ["Jitsuyo_Jidosha", "Company"], ["Jitsuyo_Jidosha", "Product"], ["1923_Great_Kantō_earthquake", "Event"], ["Jitsuyo_Jidosha", "Company"], ["World_War_I", "Event"], ["Jitsuyo_Jidosha", "Company"], ["Automobile_Industries_Company", "Company"], ["IHI_Corporation", "Company"], ["Jidōsha-Seizō_Kabushiki-Gaisha", "Company"], ["Datsun", "Product"], ["Jidōsha-Seizō_Kabushiki-Gaisha", "Company"], ["Yokohama", "Place"], ["Masujiro_Hashimoto", "Person"], ["Kaishinsha_Motorcar_Company", "Company"], ["1914", "Year"], ["DAT_Motorcar_Company", "Company"], ["Great_Kantō_earthquake", "Event"], ["Jidōsha-Seizō_Kabushiki-Gaisha", "Company"], ["Nissan_Group", "Funding"], ["Lila_(automobile)", "Product"], ["Kaishinsha_Motorcar_Company", "Company"], ["1911-07-01", "Date"], ["Jidōsha-Seizō_Kabushiki-Gaisha", "Company"], ["Tokyo", "Place"], ["Jitsuyo_Jidosha", "Company"], ["World_War_I", "Event"], ["Datsun", "Company"], ["Jidōsha-Seizō_Kabushiki-Gaisha", "Company"], ["Automobile_Industries_Company", "Company"], ["IHI_Corporation", "Company"], ["Datsun_Type_11", "Product"], ["Nissan", "Funding"], ["Kaishinsha_Motorcar_Company", "Company"], ["Great_Kantō_earthquake", "Event"], ["DAT_Motorcar_Company", "Company"], ["Lila_(automobile)", "Product"], ["Jitsuyo_Jidosha", "Company"], ["Jitsuyo_Jidosha", "Product"], ["Nissan_Group", "Funding"], ["Tokyo", "Place"], ["1914", "Year"], ["Datsun", "Product"], ["Jidōsha-Seizō_Kabushiki-Gaisha", "Company"], ["Yokohama", "Place"], ["Masujiro_Hashimoto", "Person"], ["1911-07-01", "Date"], ["World_War_I", "Event"]]


Nissan

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

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

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nissan, Beginnings of Datsun brand name from 1914: Masujiro Hashimoto (橋本増治郎) founded theKwaishinsha (Kaishinsha) Motor Car Works(快進社自働車工場,Kwaishinsha jidōsha kōjō;A Good Company Automobile Manufacturer)on 1 July 1911 inAzabu-Hiroodistrict of Tokyo. In 1914, the company produced its first car, called theDAT. The new car's model name was anacronymof the company's investors'surnames: It was renamed toKaishinsha Motorcar Co., Ltd.in 1918, and again toDAT Jidosha & Co., Ltd.(DAT Motorcar Co.) in 1925. DAT Motors built trucks in addition to the DAT and Datsun passenger cars. The vast majority of its output were trucks, due to an almost non-existent consumer market for passenger cars at the time, and disaster recovery efforts as a result of the1923 Great Kantō earthquake. Beginning in 1918, the first DAT trucks were produced for the military market. At the same time,Jitsuyo Jidosha Co., Ltd.(jitsuyo means practical use or utility) produced small trucks using parts, and materials imported from the United States.[better source needed] Commercial operations were placed on hold duringJapan's participation in World War I, and the company contributed to the war effort. In 1926, the Tokyo-based DAT Motors merged with theOsaka-based Jitsuyo Jidosha Co., Ltd(Jitsuyō Jidōsha Seizō Kabushiki-Gaisha)a.k.a.Jitsuyo Jidosha Seizo(established 1919 as aKubotasubsidiary) to becomeDAT Jidosha Seizo Co., Ltd Automobile Manufacturing Co., Ltd.(ダット自動車製造株式会社,DAT Jidōsha Seizō Kabushiki-Gaisha)in Osaka until 1932. From 1923 to 1925, the company produced light cars and trucks under the name of Lila.In 1929, DAT Automobile Manufacturing Inc. merged with a separated part of the manufacturing business ofIHI Corporationto become Automobile Industries Co., Ltd.[clarification needed] In 1931, DAT came out with a new smaller car, called theDatsun Type 11, the first "Datson", meaning "Son of DAT". Later in 1933, afterNissan Groupzaibatsutook control of DAT Motors, the last syllable of Datson was changed to "sun", because "son" also means "loss" in Japanese, hence the name "Datsun"(ダットサン,Dattosan). In 1933, the company name wasNipponizedtoJidosha-Seizo Co., Ltd.(Jidōsha Seizō Kabushiki-Gaisha; "Automobile Manufacturing Share Company")and was moved toYokohama.

[["Datsun", "founded_by", "Masujiro_Hashimoto"], ["Datsun", "has_product_line", "Datsun_Type_11"], ["Datsun", "parent_company", "Nissan"], ["Datsun", "subsidiary", "Jitsuyo_Jidosha"], ["Datsun", "subsidiary", "DAT_Motorcar"], ["Datsun", "subsidiary", "Automobile_Industries"], ["Datsun", "subsidiary", "Lila_(automobile)"], ["Datsun", "founded_by", "Kwaishinsha_Motor_Car_Works"], ["Datsun", "subsidiary", "Jidosha-Seizo"], ["Datsun", "founded_by", "IHI_Corporation"], ["Datsun", "subsidiary", "DAT_Motors"], ["Datsun", "origin", "Japan"], ["Datsun", "founded_by", "Nissan_Group_Zaibatsu"], ["Datsun", "subsidiary", "Jitsuyo_Jidosha_Seizo"], ["Datsun", "founded_by", "Tokyo"], ["Datsun", "subsidiary", "Jidōsha_Seizō_Kabushiki-Gaisha"], ["Datsun", "origin", "Azabu"], ["Datsun", "subsidiary", "Jidōsha_Motorcar"], ["Datsun", "founded_by", "Osaka"], ["Datsun", "subsidiary", "DAT_(automobile)"], ["Datsun", "founded_by", "1923_Great_Kantō_earthquake"], ["Datsun", "subsidiary", "Jidōsha_Industries"], ["Datsun", "founded_by", "World_War_I"], ["Datsun", "subsidiary", "Jidōsha_Seizō"], ["Datsun", "founded_by", "Jidōsha_Industries"], ["Datsun", "subsidiary", "Jidōsha_Motorcar_Co._Ltd."], ["Datsun", "founded_by", "Jitsuyō_Jidōsha_Seizō_Kabushiki-Gaisha"], ["Datsun", "subsidiary", "Jidōsha_Seizō_Automobile_Manufacturing_Co._Ltd."], ["Datsun", "founded_by", "Yokohama"], ["Datsun", "subsidiary", "Jidōsha_Seizō_Automobile_Manufacturing_Co._Ltd."], ["Datsun", "origin", "Tokyo"], ["Datsun", "subsidiary", "Jidōsha_Seizō"], ["Datsun", "founded_by", "Jidōsha_Seizō_Automobile_Manufacturing_Co._Ltd."], ["Datsun", "subsidiary", "Jidōsha_Seizō_Kabushiki-Gaisha"], ["Datsun", "founded_by", "Jidōsha_Seizō"], ["Datsun", "subsidiary", "Jidōsha_Motorcar_Co._Ltd."}, {"Datsun", "founded_by", "Kwaishinsha_Motor_Car_Works"], {"Datsun", "subsidiary", "Jitsuyo_Jidosha_Seizo"}, {"Datsun", "founded_by", "Jitsuyo_Jidosha_Co._Ltd."}, {"Datsun", "subsidiary", "Automobile_Industries"}, {"Datsun", "founded_by", "IHI_Corporation"}, {"Datsun", "subsidiary", "DAT_Motors"}, {"Datsun", "origin", "Japan"}, {"Datsun", "founded_by", "Nissan_Group_Zaibatsu"}, {"Datsun", "subsidiary", "Jidōsha_Seizō_Automobile_Manufacturing_Co._Ltd."}, {"Datsun", "founded_by", "1923_Great_Kantō_earthquake"}, {"Datsun", "subsidiary", "Jidōsha_Industries"}, {"Datsun", "founded_by", "World_War_I"}]


Nissan

Nissan name debut

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

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

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

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nissan, Nissan name debut: In 1928,Yoshisuke Aikawa(nickname: Gisuke/Guisuke Ayukawa) founded theholding companyNihon Sangyo(日本産業 Japan Industries or Nihon Industries). The name 'Nissan' originated during the 1930s as an abbreviationused on theTokyo Stock ExchangeforNihonSangyo. This company wasNissan"Zaibatsu" which included Tobata Casting andHitachi. At this time Nissan controlled foundries and auto parts businesses, but Aikawa did not enter automobile manufacturing until 1933. The zaibatsu eventually grew to include 74 firms and became the fourth-largest in Japan duringWorld War II.In 1931, DAT Jidosha Seizo became affiliated withTobata Castingand was merged into Tobata Casting in 1933. As Tobata Casting was a Nissan company, this was the beginning of Nissan's automobile manufacturing.

[["Nissan", "Company"], ["Yoshisuke_Aikawa", "Person"], ["Nissan", "Product"], ["1928", "Year"], ["Nissan_Zaibatsu", "Company"], ["Nihon_Sangyo", "Company"], ["Nissan_Zaibatsu", "Person"], ["World_War_II", "Date"], ["74", "number"], ["Nissan", "Company"], ["1933", "Year"], ["Tobata_Casting", "Company"], ["Nissan", "Company"], ["Tokyo_Stock_Exchange", "Place"], ["DAT_Jidosha_Seizo", "Company"]]


Nissan

Nissan name debut

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

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

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nissan, Nissan name debut: In 1928,Yoshisuke Aikawa(nickname: Gisuke/Guisuke Ayukawa) founded theholding companyNihon Sangyo(日本産業 Japan Industries or Nihon Industries). The name 'Nissan' originated during the 1930s as an abbreviationused on theTokyo Stock ExchangeforNihonSangyo. This company wasNissan"Zaibatsu" which included Tobata Casting andHitachi. At this time Nissan controlled foundries and auto parts businesses, but Aikawa did not enter automobile manufacturing until 1933. The zaibatsu eventually grew to include 74 firms and became the fourth-largest in Japan duringWorld War II.In 1931, DAT Jidosha Seizo became affiliated withTobata Castingand was merged into Tobata Casting in 1933. As Tobata Casting was a Nissan company, this was the beginning of Nissan's automobile manufacturing.

[["Nissan", "founded_by", "Yoshisuke_Aikawa"], ["Nissan", "has_nickname", "\"Gisuke\"/\"Guisuke\" Ayukawa"], ["Nissan", "debut_year_of_the_name_Nissan", "1928"], ["Nissan", "subsidiary", "DAT_Jidosha_Seizo"], ["Nissan", "parent_company", "Nihon_Sangyo"], ["Nihon_Sangyo", "has_nickname", "Nissan_Zaibatsu"], ["Nissan", "has_nickname", "Nissan"], ["Nissan_Zaibatsu", "subsidiary", "Hitachi"], ["Nissan_Zaibatsu", "subsidiary", "Tobata_Casting"], ["Nissan_Zaibatsu", "number_of_subsidiaries", "74"], ["Nissan", "carbohydrate", "World_War_II"], ["Nissan", "was_a_response_to", "Tobata_Casting"], ["Nissan", "has_nickname", "\"Nihon Sangyo\""], ["Nissan", "was_a_response_to", "1931"], ["Nissan", "has_nickname", "\"Nissan, Nissan\""], ["Nissan", "debut_year_of_the_name_Nissan", "1933"], ["Nissan", "has_nickname", "Nihon_Sangyo"], ["Nissan", "was_a_response_to", "Tokyo_Stock_Exchange"]]


Nissan

Nissan Motor

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

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

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

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nissan, Nissan Motor: In 1934, Aikawa separated the expanded automobile parts division of Tobata Casting and incorporated it as a new subsidiary, which he namedNissan Motor Co., Ltd.(日産自動車,Nissan Jidōsha).The shareholders of the new company; however, were not enthusiastic about the prospects of the automobile in Japan, so Aikawa bought out all the Tobata Casting shareholders (using capital from Nihon Industries) in June 1934. At this time, Nissan Motor effectively became owned byNihon Sangyo and Hitachi. In 1935, the construction of its Yokohama plant was completed. 44 Datsuns were shipped to Asia, Central and South America. In 1935, the first car manufactured by anintegrated assembly systemrolled off the line at the Yokohama plant.Nissan built trucks, airplanes, and engines for theImperial Japanese Army. In November 1937 Nissan moved its headquarters toXinjing, the capital ofManchukuo. In December the company changed its name toManchuria Heavy Industries Developing Co(MHID). In 1940, the first knockdown kits were shipped toDowa Jidosha Kogyo (Dowa Automobile), one of MHID's companies, for assembly.In 1944, the head office was moved toNihonbashi, Tokyo, and the company name was changed to Nissan Heavy Industries, Ltd., which the company kept through 1949.

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