Nissan

Alliance with Renault and Mitsubishi

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 and Mitsubishi: In 1999, facing severe financial difficulties, Nissan entered an alliance withRenaultof France.Signed on 27 March 1999, theRenault-Nissan Alliancewas the first of its kind involving a Japanese and French car manufacturer, each with its own distinct corporate culture and brand identity. Renault initially acquired a 36.8 percent stake in Nissan for $3.5 billion pending court approval and Nissan permanently vowed to buy into Renault when it was financially able. In 2001, after the company's turnaround from near-bankruptcy, Nissan acquired a 15 percent share of Renault, which in turn increased its stake in Nissan to 43.4 percent. The Renault-Nissan Alliance has evolved over the years to Renault holding 43.4 percent of Nissan shares, while Nissan held 15 percent of Renault shares. The alliance itself was incorporated as the Renault-Nissan B.V., founded on 28 March 2002 under Dutch law. Renault-Nissan B.V. is equally owned by Renault and Nissan. On 7 April 2010,Daimler AGexchanged a 3.1 percent share of its holdings for 3.1 percent from both Nissan and Renault. This triple alliance allows for the increased sharing of technology and development costs, encouraging global cooperation and mutual development. On 12 December 2012, the Renault–Nissan Alliance formed a joint venture with Russian Technologies (Alliance Rostec Auto BV) with the aim of becoming the long-term controlling shareholder ofAvtoVAZ, Russia's largest car company and owner of the country's biggest selling brand,Lada.The takeover was completed in June 2014, and the two companies of the Renault-Nissan Alliance took a combined 67.1 percent stake of Alliance Rostec, which in turn acquired a 74.5 percent of AvtoVAZ, thereby giving Renault and Nissan indirect control over the Russian manufacturer.Ghosn was appointed chairman of the board of AvtoVAZ on 27 June 2013.In September 2017, Nissan sold its AvtoVAZ stake to Renault for Euro 45 million. Taken together, in 2013 the Renault–Nissan Alliance sold one in ten cars worldwide, and would be the world's fourth largest automaker with sales of 8,266,098 units. Renault and Nissan has announced the change in stock share between two companies, in March 2023. With this announcement and change, both companies will be treated as economically independent entity.

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"populationTotal': '1299', "Renault", "hasDeputy': 'Takao Katô', "Renault", "ethnicGroup': 'French people', "Renault", "leaderTitle': 'Chairman of the Board', "Renault", "elevationAboveTheSeaLevel': '44.0", "Renault", "city": "Boulogne-Billancourt", "Renault", "industry': "Automotive', "Renault", "areaTotal': "2.6 (square kilometres)", "Renault", "foundationYear': "1899', "Renault", "longName": "Renault S.A.', "Renault", "isPartOf": "Renault–Nissan Alliance', "Renault", "parent': "N/A', "Renault", "economy': 'Automotive', "Renault", "birthPlace': "France', "Renault", "location': 'France', "Renault", "leader': 'Carlos Ghosn', "Renault", "division': 'Renault Sport', "Renault", "foundingDate": "1999-03-27", "Renault", "numberOfLocations": '1", "Renault", "numberOfEmployees": "44480", "Renault", "keyPerson": "Carlos Ghosn", "Renault", "carbohydrate': '0.0", "Renault", "capital": "Paris", "Renault", "type': "Company', "Renault", "parentCompany': 'N/A', "Renault", "product': 'Automotive', "Renault", "carBrand': 'Renault', "Renault", "subsidiary': 'Nissan', "Renault", "regionServed': 'Worldwide', "Renault", "foundationPlace': 'France', "Renault", "populationTotal': '1299', "Renault", "hasDeputy': 'Takao Katô', "Renault", "ethnicGroup': 'French people', "Renault", "leaderTitle': 'Chairman of the Board', "Renault", "elevationAboveTheSeaLevel': '44.0', "Renault", "city': 'Boulogne-Billancourt', "Renault", "industry': 'Automotive', "Renault", "areaTotal': '2.6 (square kilometres)', "Renault", "foundationYear': '1899', "Renault", "longName": "Renault S.A.', "Renault", "isPartOf": "Renault–Nissan Alliance', "Renault", "parent': 'N/A', "Renault", "economy': 'Automotive', "Renault", "birthPlace': 'France', "Renault", "location': 'France', "Renault", "leader': 'Carlos Ghosn', "Renault", "division': 'Renault Sport', "Renault", "foundingDate": "1999-03-27", "Renault", "numberOfLocations": '1", "Renault", "numberOfEmployees": "44480", "Renault", "keyPerson': "Carlos Ghosn", "Renault", "carbohydrate': "0.0", "Renault", "capital": "Paris", "Renault", "type': "Company', "Renault", "parentCompany': 'N/A', "Renault", "product': 'Automotive', "Renault", "carBrand': 'Renault', "Renault", "subsidiary': 'Nissan', "Renault", "regionServed': 'Worldwide', "Renault", "foundationPlace': 'France', "Renault", "populationTotal': '1299', "Renault", "hasDeputy': 'Takao Katô', "Renault", "ethnicGroup': 'French people', "Renault", "leaderTitle': 'Chairman of the Board', "Renault", "elevationAboveTheSeaLevel": "44.0', "Renault", "city": "Boulogne-Billancourt', "Renault", "industry': 'Automotive', "Renault", "areaTotal": '2.6 (square kilometres)', "Renault", "foundationYear": '1899', "Renault", "longName": "Renault S.A.', "Renault", "isPartOf": "Renault–Nissan Alliance', "Renault", "parent': "N/A', "Renault", "economy": 'Automotive', "Renault", "birthPlace': 'France', "Renault", "location": 'France', "Renault", "leader": 'Carlos Ghosn', "Renault", "division": 'Renault Sport', "Renault", "foundingDate": "1999-03-27", "Renault", "numberOfLocations": '1", "Renault", "numberOfEmployees": "44480", "Renault", "keyPerson": "Carlos Ghosn", "Renault", "carbohydrate": "0.0", "Renault", "capital": "Paris", "Renault", "type': "Company', "Renault", "parentCompany': "N/A', "Renault", "product': 'Automotive', "Renault", "carBrand': 'Renault', "Renault", "subsidiary': 'Nissan', "Renault", "regionServed': 'Worldwide', "Renault", "foundationPlace': 'France', "Renault", "populationTotal": '1299', "Renault", "hasDeputy': 'Takao Katô', "Renault", "ethnicGroup': 'French people', "Renault", "leaderTitle": "Chairman of the Board', "Renault", "elevationAboveTheSeaLevel": '44.0', "Renault", "city": "Boulogne-Billancourt', "Renault", "industry': 'Automotive', "Renault", "areaTotal": '2.6 (square kilometres)', "Renault", "foundationYear": '1899', "Renault", "longName": "Renault S.A.', "Renault", "isPartOf": "Renault–Nissan Alliance', "Renault", "parent': "N/A', "Renault", "economy": 'Automotive', "Renault", "birthPlace': 'France', "Renault", "location": 'France', "Renault", "leader': 'Carlos Ghosn', "Renault", "division": 'Renault Sport', "Renault", "foundingDate": "1999-03-27", "Renault", "numberOfLocations": '1", "Renault", "numberOfEmployees": "44480", "Renault", "keyPerson": "Carlos Ghosn", "Renault", "carbohydrate": "0.0", "Renault", "capital': "Paris", "Renault", "type': 'Company', "Renault", "parentCompany': 'N/A', "Renault", "product': 'Automotive', "Renault", "carBrand': 'Renault', "Renault", "subsidiary': 'Nissan', "Renault", "regionServed': 'Worldwide', "Renault", "foundationPlace': 'France', "Renault", "populationTotal': '1299', "Renault", "hasDeputy': 'Takao Katô', "Renault", "ethnicGroup': 'French people', "Renault", "leaderTitle": "Chairman of the Board', "Renault", "elevationAboveTheSeaLevel": '44.0', "Renault", "city":


Nissan

Alliance with Renault and Mitsubishi

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, Alliance with Renault and Mitsubishi: In 1999, facing severe financial difficulties, Nissan entered an alliance withRenaultof France.Signed on 27 March 1999, theRenault-Nissan Alliancewas the first of its kind involving a Japanese and French car manufacturer, each with its own distinct corporate culture and brand identity. Renault initially acquired a 36.8 percent stake in Nissan for $3.5 billion pending court approval and Nissan permanently vowed to buy into Renault when it was financially able. In 2001, after the company's turnaround from near-bankruptcy, Nissan acquired a 15 percent share of Renault, which in turn increased its stake in Nissan to 43.4 percent. The Renault-Nissan Alliance has evolved over the years to Renault holding 43.4 percent of Nissan shares, while Nissan held 15 percent of Renault shares. The alliance itself was incorporated as the Renault-Nissan B.V., founded on 28 March 2002 under Dutch law. Renault-Nissan B.V. is equally owned by Renault and Nissan. On 7 April 2010,Daimler AGexchanged a 3.1 percent share of its holdings for 3.1 percent from both Nissan and Renault. This triple alliance allows for the increased sharing of technology and development costs, encouraging global cooperation and mutual development. On 12 December 2012, the Renault–Nissan Alliance formed a joint venture with Russian Technologies (Alliance Rostec Auto BV) with the aim of becoming the long-term controlling shareholder ofAvtoVAZ, Russia's largest car company and owner of the country's biggest selling brand,Lada.The takeover was completed in June 2014, and the two companies of the Renault-Nissan Alliance took a combined 67.1 percent stake of Alliance Rostec, which in turn acquired a 74.5 percent of AvtoVAZ, thereby giving Renault and Nissan indirect control over the Russian manufacturer.Ghosn was appointed chairman of the board of AvtoVAZ on 27 June 2013.In September 2017, Nissan sold its AvtoVAZ stake to Renault for Euro 45 million. Taken together, in 2013 the Renault–Nissan Alliance sold one in ten cars worldwide, and would be the world's fourth largest automaker with sales of 8,266,098 units. Renault and Nissan has announced the change in stock share between two companies, in March 2023. With this announcement and change, both companies will be treated as economically independent entity.

[["Renault", "has_product_line", "Lada"], ["Renault", "has_product_line", "Nissan"], ["Renault", "has_product_line", "AvtoVAZ"], ["Renault", "shares", "Renault-Nissan Alliance"], ["Renault", "shares", "Alliance Rostec Auto BV"], ["Renault", "shares", "Daimler"], ["Renault", "shares", "Nissan"], ["Renault", "subsidiary", "AvtoVAZ"], ["Renault", "subsidiary", "Renault-Nissan B.V."], ["Renault", "shares", "Gustave Eiffel"], ["Renault", "shares", "Carlos Ghosn"], ["Renault", "shares", "French_language"], ["Renault", "shares", "Japanese_language"], ["Renault", "shares", "Nissan_Motor"], ["Renault", "shares", "Renault"], ["Renault", "shares", "Robert_Bosch_Corporation"], ["Renault", "shares", "Mitsubishi_Fuso_Truck_and_Bus"], ["Renault", "shares", "Mitsubishi"], ["Renault", "shares", "Proton"], ["Renault", "shares", "DaimlerChrysler"], ["Renault", "shares", "Opel"], ["Renault", "shares", "Pontiac"], ["Renault", "shares", "Saab_Automobile"], ["Renault", "shares", "Citroën"], ["Renault", "shares", "Peugeot"], ["Renault", "shares", "Fiat_Chrysler"], ["Renault", "shares", "Fiat"], ["Renault", "shares", "Alfa_Romeo"], ["Renault", "shares", "Lancia"], ["Renault", "shares", "Abarth"], ["Renault", "shares", "Maserati"], ["Renault", "shares", "BMW"], ["Renault", "shares", "Mercedes-Benz"], ["Renault", "shares", "Smart_Fortwo"], ["Renault", "shares", "Daewoo"], ["Renault", "shares", "Ferrari"], ["Renault", "shares", "Volkswagen"], ["Renault", "shares", "Audi"], ["Renault", "shares", "Seat"], ["Renault", "shares", "Skoda"], ["Renault", "shares", "Iveco"], ["Renault", "shares", "Suzuki"], ["Renault", "shares", "Toyota"], ["Renault", "shares", "Daimler"], ["Renault", "subsidiary", "Renault_Developpement_Services"], ["Renault", "subsidiary", "Renault_Finance"], ["Renault", "subsidiary", "Renault_Trucks"], ["Renault", "subsidiary", "Renault_Sport"], ["Renault", "subsidiary", "Renaissance_Auto"], ["Renault", "subsidiary", "Sofim"], ["Renault", "subsidiary", "Société_Anonyme_Des_Automobiles_Renault"], ["Renault", "subsidiary", "Mitsubishi_Motors"], ["Renault", "subsidiary", "Nissan"], ["Renault", "subsidiary", "DaimlerChrysler"], ["Renault", "subsidiary", "AvtoVAZ"], ["Renault", "subsidiary", "American_Locomotive_Company"], ["Renault", "subsidiary", "Fiat"], ["Renault", "subsidiary", "Alfa_Romeo"], ["Renault", "subsidiary", "Abarth"], ["Renault", "subsidiary", "Lancia"], ["Renault", "subsidiary", "Maserati"], ["Renault", "subsidiary", "Opel"], ["Renault", "subsidiary", "Pontiac"], ["Renault", "subsidiary", "Saab_Automobile"], ["Renault", "subsidiary", "Proton"], ["Renault", "subsidiary", "Renault-Nissan_Alliance"], ["Renault", "subsidiary", "Renault-Nissan_B.V."], ["Renault", "subsidiary", "Alliance_Renault-Nissan"], ["Renault", "subsidiary", "Renault_Developpement_Services"], ["Renault", "subsidiary", "Renault_Finance"], ["Renault", "subsidiary", "Renault_Trucks"], ["Renault", "subsidiary", "Renault_Sport"], ["Renault", "subsidiary", "Renaissance_Auto"], ["Renault", "subsidiary", "Sofim"], ["Renault", "subsidiary", "Société_Anonyme_Des_Automobiles_Renault"], ["Renault", "subsidiary", "Mitsubishi_Motors"], ["Renault", "subsidiary", "Nissan"], ["Renault", "subsidiary", "DaimlerChrysler"], ["Renault", "subsidiary", "AvtoVAZ"], ["Renault", "subsidiary", "American_Locomotive_Company"], ["Renault", "subsidiary", "Fiat"], ["Renault", "subsidiary", "Alfa_Romeo"], ["Renault", "subsidiary", "Abarth"], ["Renault", "subsidiary", "Lancia"], ["Renault", "subsidiary", "Maserati"], ["Renault", "subsidiary", "Opel"], ["Renault", "subsidiary", "Pontiac"], ["Renault", "subsidiary", "Saab_Automobile"], ["Renault", "subsidiary", "Proton"], ["Renault", "subsidiary", "Renault-Nissan_Alliance"], ["Renault", "subsidiary", "Renault-Nissan_B.V."}, {"Renault", "has_stake_in", "Nissan"}, {"Renault", "has_stake_in", "Mitsubishi"}, {"Renault", "has_stake_in", "Renault-Nissan_Alliance"}, {"Renault", "has_stake_in", "Renault-Nissan_B.V."}, {"Renault", "has_stake_in", "Alliance_Renault-Nissan"}, {"Renault", "has_stake_in", "AvtoVAZ"}, {"Renault", "has_stake_in", "American_Locomotive_Company"}, {"Renault", "has_stake_in", "Fiat"}, {"Renault", "has_stake_in", "Alfa_Romeo"}, {"Renault", "has_stake_in", "Abarth"}, {"Renault", "has_stake_in", "Lancia"}, {"Renault", "has_stake_in", "Maserati"}, {"Renault", "has_stake_in", "Opel"}, {"Renault", "has_stake_in", "Pontiac"}, {"Renault", "has_stake_in", "Saab_Automobile"}, {"Renault", "has_stake_in", "Proton"}, {"Renault", "has_stake_in", "DaimlerChrysler"}, {"Renault", "has_stake_in", "Suzuki"}, {"Renault", "has_stake_in", "Toyota"}, {"Renault", "has_stake_in", "Ferrari"}, {"Renault", "has_stake_in", "BMW"}, {"Renault", "has_stake_in", "Mercedes-Benz"}]


Nissan

Brands

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, Brands: Nissan:Nissan's volume models are sold worldwide under the Nissan brand. Datsun:Until 1983, Nissan automobiles in most export markets were sold under theDatsunbrand. In 1984 the Datsun brand was phased out and the Nissan brand was phased in. All cars in 1984 had both the Datsun and Nissan branding on them and in 1985 the Datsun name was completely dropped. In July 2013, Nissan relaunched Datsun as a brand targeted at emerging markets. However, due to sluggish sales, Nissan ended sales of Datsun-badged vehicles in 2022. Infiniti:Since 1989, Nissan has sold its luxury models under theInfinitibrand. In 2012, Infiniti changed its headquarters toHong Kong, where it is incorporated as Infiniti Global Limited. Its president is former BMW executive Roland Krueger. From 2014 to 2020, the Japanese-marketSkyline(rebadgedInfiniti Q50) andFuga(rebadgedInfiniti Q70) were sold with Infiniti emblem. Nismo:Nissan's in-house tuning shop isNismo, short for "Nissan Motorsport International Limited." Nismo is being re-positioned as Nissan's performance brand.

[["Nissan", "Company"], ["Nissan", "Product"], ["Datsun", "Product"], ["Nissan", "Person"], ["Infiniti", "Product"], ["Infiniti", "Company"], ["Hong_Kong", "Location"], ["Nissan", "City"], ["Nissan", "Country"], ["Nismo", "Product"], ["Nissan", "Brand"], ["Nissan", "Person"], ["Nissan", "Company"], ["Datsun", "Company"], ["2022", "Year"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", 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"Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan", "Country"], ["Nissan", "Product"], ["Nissan", "Company"], ["Nissan", "Person"], ["Nissan", "City"], ["Nissan


Nissan

Brands

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, Brands: Nissan:Nissan's volume models are sold worldwide under the Nissan brand. Datsun:Until 1983, Nissan automobiles in most export markets were sold under theDatsunbrand. In 1984 the Datsun brand was phased out and the Nissan brand was phased in. All cars in 1984 had both the Datsun and Nissan branding on them and in 1985 the Datsun name was completely dropped. In July 2013, Nissan relaunched Datsun as a brand targeted at emerging markets. However, due to sluggish sales, Nissan ended sales of Datsun-badged vehicles in 2022. Infiniti:Since 1989, Nissan has sold its luxury models under theInfinitibrand. In 2012, Infiniti changed its headquarters toHong Kong, where it is incorporated as Infiniti Global Limited. Its president is former BMW executive Roland Krueger. From 2014 to 2020, the Japanese-marketSkyline(rebadgedInfiniti Q50) andFuga(rebadgedInfiniti Q70) were sold with Infiniti emblem. Nismo:Nissan's in-house tuning shop isNismo, short for "Nissan Motorsport International Limited." Nismo is being re-positioned as Nissan's performance brand.

[["Nissan", "has_product_line", "Nissan"], ["Nissan", "has_product_line", "Datsun"], ["Datsun", "occurred_in_year", "1983"], ["Datsun", "occurred_in_year", "1984"], ["Datsun", "occurred_in_year", "2013"], ["Datsun", "occurred_in_year", "2022"], ["Nissan", "has_product_line", "Infiniti"], ["Infiniti", "has_product_line", "Skyline"], ["Infiniti", "has_product_line", "Fuga"], ["Infiniti", "has_product_line", "Q50"], ["Infiniti", "has_product_line", "Q70"], ["Infiniti", "has_CEO", "Roland_Krueger"], ["Infiniti", "has_CEO", "BMW"], ["Infiniti", "has_headquarters", "Hong_Kong"], ["Infiniti", "has_headquarters", "Infiniti_Global_Limited"], ["Nissan", "has_product_line", "Nismo"], ["Nismo", "has_full_name", "\"Nissan Motorsport International Limited\""]]


Nissan

Corporate identity

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, Corporate identity: For many years, Nissan used a red wordmark for the company, and car badges for the "Nissan" and "Infiniti" brands.At Nissan's 2013 earnings press conference in Yokohama, Nissan unveiled "a new steel-blue logo that spells out—literally—the distinction between Nissan the company and Nissan the brand."Using a blue-gray color scheme, the new corporate logo did read NISSAN MOTOR COMPANY. Underneath were the "badge" logos for the Nissan, Infiniti and Datsun brands. Later in 2013, the Nissan "Company" logo changed to the Nissan "Corporation" logo. The latter was the logo used by Nissan Motor Co., Ltd.up to early 2020. In July 2020, Nissan introduced new corporate and brand logos, as part of an image revamp tied to theAriyalaunch. As of 2007 in Japan, Nissan sells its products with internationally recognized "Nissan" signage, using a chrome circle with "Nissan" across the front. Previously, Nissan used two dealership names called "Nissan Blue Stage"(ja:日産・ブルーステージ,Nissan Burū Sutēji), "Nissan Red Stage"(ja:日産・レッドステージ,Nissan Reddo Sutēji), and "Nissan Red and Blue Stage"(ja:日産・レッド&ブルーステージ,Nissan Reddo & Burū Sutēji), established in 1999 after forming an alliance withRenault. Renault also exported cars to Japan and were available at "Nissan Red Stage" locations, and are still available at Nissan Japanese dealerships. Nissan Red Stage was the result of combining an older sales channel of dealerships under the names "Nissan Prince Store"(ja:日産・プリンス店,Nissan Purinsu-ten), established in 1966 after the merger ofPrince Motorsby Nissan, which sold theNissan Skyline. "Nissan Satio Store"(日産・サティオ店,Nissan Satio-ten)sold cars developed from theNissan Sunnyat its introduction in 1966. The word "satio" is Latin, which meansampleorsufficient. "Nissan Cherry Store"(日産・チェリー店,Nissan Cherī-ten)was briefly known previously as "Nissan Cony Store" when they assumed operations of a smallkeimanufacturer calledAichi Machine Industry Co., Ltd. (愛知機械工業)who manufactured the "Cony", "Guppy" and"Giant"brand ofkeicars and trucks until 1970, when the network was renamed for theNissan Cherry. Nissan Blue Stage was the result of combining older sales channels, called "Nissan Store"(ja:日産店,Nissan Mise)in 1955 selling Nissan's original post-war products called theDatsun Bluebird,Datsun Sports,Datsun Truck,Datsun Cablight,Datsun Cabstar,Nissan Juniorand theNissan Patrol. Select "Nissan Store" locations were renamed "Nissan Cedric Store" when theNissan Cedricwas introduced in 1960, then renamed "Nissan Store" in 1965 and offered luxury sedans like theNissan Presidentand the former Prince Motor Company developedNissan Laurel. In 1970, Nissan also set up a separate sales chain which sold used cars including auctions, called Nissan U-Cars(ja:日産ユーズドカーセンター,Nissan Yūzudo Kā Sentā), which they still maintain. In the early days of Nissan's dealership network, Japanese consumers were directed towards specific Nissan stores for cars that were of a specific size and pricepoint. Over time as sales progressed and the Japanese automotive industry became more prolific, vehicles that were dedicated to particular stores werebadge engineered, given different names, and shared within the existing networks thereby selling the same platforms at different locations. The networks allowed Nissan to better compete with the network established earlier byToyotaat Japanese locations.
Starting in 1960, another sales distribution channel was established that sold diesel products for commercial use, calledNissan Dieseluntil the diesel division was sold in 2007 toVolvo. To encourage retail sales, Nissan passenger vehicles that were installed with diesel engines, like the Cedric, were available at Nissan Diesel locations. All cars sold at Nissan Blue Stage (1999–2005): All cars sold at Nissan Store, (1955–1999): All cars sold at Nissan Red Stage (1999–2005): All cars sold at Nissan Prince Store, Nissan Satio Store, Nissan Cherry Store (1966–1999):

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