Nissan

Nissan Motor

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

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

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nissan, Nissan Motor: In 1934, Aikawa separated the expanded automobile parts division of Tobata Casting and incorporated it as a new subsidiary, which he namedNissan Motor Co., Ltd.(日産自動車,Nissan Jidōsha).The shareholders of the new company; however, were not enthusiastic about the prospects of the automobile in Japan, so Aikawa bought out all the Tobata Casting shareholders (using capital from Nihon Industries) in June 1934. At this time, Nissan Motor effectively became owned byNihon Sangyo and Hitachi. In 1935, the construction of its Yokohama plant was completed. 44 Datsuns were shipped to Asia, Central and South America. In 1935, the first car manufactured by anintegrated assembly systemrolled off the line at the Yokohama plant.Nissan built trucks, airplanes, and engines for theImperial Japanese Army. In November 1937 Nissan moved its headquarters toXinjing, the capital ofManchukuo. In December the company changed its name toManchuria Heavy Industries Developing Co(MHID). In 1940, the first knockdown kits were shipped toDowa Jidosha Kogyo (Dowa Automobile), one of MHID's companies, for assembly.In 1944, the head office was moved toNihonbashi, Tokyo, and the company name was changed to Nissan Heavy Industries, Ltd., which the company kept through 1949.

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'range': 'Person'}}, {'domain': 'Nissan_Motor', 'range': 'Person'}}, {'domain': 'Nissan_Motor', 'range': 'Person'}}, {'domain': 'Nissan_Motor', 'range': 'Person'}}, {'domain': 'Nissan_Motor', 'range': 'Person'}}, {'domain': 'Nissan_Motor', 'range': 'Person'}}, {'domain': 'Nissan_Motor', 'range': 'Person'}}, {'domain': 'Nissan_Motor', 'range': 'Person'}}, {'domain': 'Nissan_Motor', 'range': 'Person'}}, {'domain': 'Nissan_Motor', 'range': 'Person'}}, {'domain': 'Nissan_Motor', 'range': 'Person'}}, {'domain': 'Nissan_Motor', 'range': 'Person'}}, {'domain': 'Nissan_Motor', 'range': 'Person'}}, {'domain': 'Nissan_Motor',


Nissan

American market and expansion

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

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

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

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nissan, American market and expansion: DAT had inheritedKubota's chief designer, American engineerWilliam R. Gorham. This, along with Aikawa's 1908 visit to Detroit, was to greatly affect Nissan's future.Although it had always been Aikawa's intention to use cutting-edge auto making technology from America, it was Gorham that carried out the plan. Most of the machinery and processes originally came from the United States. When Nissan started to assemble larger vehicles under the "Nissan" brand in 1937, much of the design plans and plant facilities were supplied by theGraham-PaigeCompany.Nissan also had a Graham license under which passenger cars, buses, and trucks were made. In his 1986 bookThe Reckoning,David Halberstamstates "In terms of technology, Gorham was the founder of the Nissan Motor Company" and that "young Nissan engineers who had never met him spoke of him as a god and could describe in detail his years at the company and his many inventions."

[["Nissan", "Company"], ["William_R._Gorham", "Person"], ["Graham-Paige_Company", "Company"], ["Nissan", "Product"], ["United_States", "Place"], ["American_engineer", "Person"], ["Nissan", "Company"], ["Gorham", "Person"], ["Nissan", "Company"], ["American_market", "Place"], ["Nissan", "Company"], ["DAT", "Company"], ["Nissan", "Company"], ["Graham_Paige", "Company"], ["Nissan", "Product"], ["Nissan", "Company"], ["Aikawa", "Person"], ["Detroit", "Place"], ["Nissan", "Company"], ["Gorham", "Person"], ["Nissan", "Company"], ["United_States", "Place"], ["Nissan", "Company"], ["Gorham", "Person"], ["Nissan", "Company"], ["Graham_Paige", "Company"], ["Nissan", "Product"], ["Nissan", "Company"], ["Aikawa", "Person"], ["Detroit", "Place"], ["Nissan", "Company"], ["Gorham", "Person"], ["Nissan", "Company"], ["Graham_Paige", "Company"], ["Nissan", "Product"], ["Nissan", "Company"], ["United_States", "Place"], ["Nissan", "Company"], ["Gorham", "Person"], ["Nissan", "Company"], ["Graham_Paige", "Company"], ["Nissan", "Product"], ["Nissan", "Company"], ["Aikawa", "Person"], ["Detroit", "Place"], ["Nissan", "Company"], ["Gorham", "Person"], ["Nissan", "Company"], ["Graham_Paige", "Company"], ["Nissan", "Product"], ["Nissan", "Company"], ["United_States", "Place"], ["Nissan", "Company"], ["Gorham", "Person"], ["The_Reckoning_(book)", "WrittenWork"], ["Nissan", "Company"], ["Gorham", "Person"], ["Nissan", "Company"], ["Graham_Paige", "Company"], ["Nissan", "Product"], ["Nissan", "Company"], ["Aikawa", "Person"], ["Detroit", "Place"], ["Nissan", "Company"], ["Gorham", "Person"], ["Nissan", "Company"], ["Graham_Paige", "Company"], ["Nissan", "Product"], ["Nissan", "Company"], ["United_States", "Place"], ["Nissan", "Company"], ["Gorham", "Person"], ["Nissan", "Company"], ["Graham_Paige", "Company"], ["Nissan", "Product"]]


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."

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"Aikawa_Mitsubishi_Industries"], ["Nissan", "influenced_by", "Gorham"], ["Nissan", "influenced_by", "Graham-Paige"], ["Nissan", "influenced_by", "United_States"], ["Nissan", "influenced_by", "Aikawa_Mitsubishi_Industries"], ["Nissan", "influenced_by", "Gorham"], ["Nissan", "influenced_by", "Graham-Paige"], ["Nissan", "influenced_by", "United_States"], ["Nissan", "influenced_by", "Aikawa_Mitsubishi_Industries"], ["Nissan", "influenced_by", "Gorham"], ["Nissan", "influenced_by", "Graham-Paige"], ["Nissan", "influenced_by", "United_States"], ["Nissan", "influenced_by", "Aikawa_Mitsubishi_Industries"], ["Nissan", "influenced_by", "Gorham"], ["Nissan", "influenced_by", "Graham-Paige"], ["Nissan", "influenced_by", "United_States"], ["Nissan", "influenced_by", "Aikawa_Mitsubishi_Industries"], ["Nissan", "influenced_by", "Gorham"], ["Nissan", "influenced_by", "Graham-Paige"], ["Nissan", "influenced_by", "United_States"], ["Nissan", "influenced_by", "Aikawa_Mitsubishi_Industries"], ["Nissan", 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"influenced_by", "Aikawa_Mitsubishi_Industries"}, {"Nissan", "influenced_by", "Gorham"}, {"Nissan", "influenced_by", "Graham-Paige"}, {"Nissan", "influenced_by", "United_States"}, {"Nissan", "influenced_by", "Aikawa_Mitsubishi_Industries"}, {"Nissan", "influenced_by", "Gorham"}, {"Nissan", "influenced_by", "Graham-Paige"}, {"Nissan", "influenced_by", "United_States"}, {"Nissan", "influenced_by", "Aikawa_Mitsubishi_Industries"}, {"Nissan", "influenced_by", "Gorham"}, {"Nissan", "influenced_by", "Graham-Paige"}, {"Nissan", "influenced_by", "United_States"}, {"Nissan", "influenced_by", "Aikawa_Mitsubishi_Industries"}, {"Nissan", "influenced_by", "Gorham"}, {"Nissan", "influenced_by", "Graham-Paige"}, {"Nissan", "influenced_by", "United_States"}, {"Nissan", "influenced_by", "Aikawa_Mitsubishi_Industries"}, {"Nissan", "influenced_by", "Gorham"}, {"Nissan", "influenced_by", "Graham-Paige"}, {"Nissan", "influenced_by", "United_States"}, {"Nissan", "influenced_by", "Aikawa_Mitsubishi_Industries"}, {"Nissan", "influenced_by", "Gorham"}, {"Nissan", "influenced_by", "Graham-Paige"}, {"Nissan", "influenced_by", "United_States"}, {"Nissan", "influenced_by", "Aikawa_Mitsubishi_Industries"}, {"Nissan", "influenced_by", "Gorham"}, {"Nissan", "influenced_by", "Graham-Paige"}, {"Nissan", "influenced_by", "United_States"}, {"Nissan", "influenced_by", "Aikawa_Mitsubishi_Industries"}, {"Nissan", "influenced_by", "Gorham"}, {"Nissan", "influenced_by", "Graham-Paige"}, {"Nissan", "influenced_by", "United_States"}, {"Nissan", "influenced_by", "Aikawa_Mitsubishi_Industries"}, {"Nissan", "influenced_by", "Gorham"}, {"Nissan", "influenced_by", "Graham-Paige"}, {"Nissan", "influenced_by", "United_States"}, {"Nissan", "influenced_by", "Aikawa_Mitsubishi_Industries"}, {"Nissan", "influenced_by", "Gorham"}, {"Nissan", "influenced_by", "Graham-Paige"}, {"Nissan", "influenced_by", "United_States"}, {"Nissan", "influenced_by", "Aikawa_Mitsubishi_Industries"}, {"Nissan", "influenced_by", "Gorham"}, {"Nissan", "influenced_by", "Graham-Paige"}, {"Nissan", "influenced_by", "United_States"}, {"Nissan", "influenced_by", "Aikawa_Mitsubishi_Industries"}, {"Nissan", "influenced_by", "Gorham"}, {"Nissan", "influenced_by", "Graham-Paige"}, {"Nissan", "influenced_by", "United_States"}, {"Nissan", "influenced_by", "Aikawa_Mitsubishi_Industries"}, {"Nissan", "influenced_by", "Gorham"}, {"Nissan", "influenced_by", "Graham-Paige"}, {"Nissan", "influenced_by", "United_States"}, {"Nissan", "influenced_by", "Aikawa_Mitsubishi_Industries"}, {"Nissan", "influenced_by", "Gorham"}, {"Nissan", "influenced_by", "Graham-Paige"}, {"Nissan", "influenced_by", "United_States"}, {"Nissan", "influenced_by", "Aikawa_Mitsubishi_Industries"}, {"Nissan", "influenced_by", "Gorham"}, {"Nissan", "influenced_by", "Graham-Pa


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."

[["Nissan", "Company"], ["Datsun", "Company"], ["Austin_Motor_Company", "Company"], ["Datsun", "Product"], ["Austin_Motor_Company", "Company"], ["Nissan", "Person"], ["United_States", "Country"], ["Korean_War", "Event"], ["Shioji_Ichiro", "Person"], ["Harvard_University", "School"], ["Datsun_A50", "Product"], ["Nissan_L_engine", "string"], ["Datsun_240Z", "Product"], ["Nissan_Machinery", "Company"], ["Kurogane", "Company"], ["Japanese_labor_movement", "Organisation"], ["1955–1973", "Period"], ["United_States", "Country"], ["Japanese_labor_movement", "Company"], ["Shioji_Ichiro", "Person"], ["Harvard_University", "School"], ["Datsun_510", "Product"], ["Mercedes-Benz", "Company"], ["Nissan", "Company"], ["Datsun", "Product"], ["United_States", "Country"], ["Korean_War", "Event"], ["Japanese_labor_movement", "Company"], ["Shioji_Ichiro", "Person"], ["Harvard_University", "School"], ["Datsun_A50", "Product"], ["Nissan_L_engine", "string"], ["Datsun_240Z", "Product"], ["Nissan_Machinery", "Company"], ["Kurogane", "Company"], ["1955–1973", "Period"], ["United_States", "Country"], ["Japanese_labor_movement", "Company"], ["Shioji_Ichiro", "Person"], ["Harvard_University", "School"], ["Datsun_510", "Product"], ["Mercedes-Benz", "Company"]]


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", "produced", "Austin_1800"], ["Nissan", "produced", "Datsun"], ["Nissan", "produced", "Datsun_240Z"], ["Datsun_240Z", "engine", "Nissan_L_engine"], ["Nissan_L_engine", "designCompany", "Mercedes-Benz"], ["Nissan_L_engine", "designCompany", "Nissan"], ["Nissan", "hasProduct", "Datsun_510"], ["Nissan", "hasProduct", "Nissan_A_series_engine"], ["Nissan", "hasProduct", "Austin_7"], ["Nissan", "hasProduct", "Austins"], ["Nissan", "hasProduct", "Aston_Martin_V8"], ["Nissan", "hasProduct", "Aston_Martin_Rapide"], ["Nissan", "hasProduct", "Aston_Martin_Vantage"], ["Nissan", "hasProduct", "Aston_Martin_DB9"], ["Nissan", "hasProduct", "Aston_Martin_Virage"], ["Nissan", "hasProduct", "Aston_Martin_One_77"], ["Nissan", "hasProduct", "Aston_Martin_Roadster"], ["Nissan", "hasProduct", "Aston_Martin_DBS"], ["Nissan", "hasProduct", "Aston_Martin_V8_Vantage"], ["Nissan", "hasProduct", "Aston_Martin_Sports_Car"], ["Nissan", "hasProduct", "Aston_Martin_F1"], ["Nissan", "hasProduct", "Aston_Martin_Renault_V12"], ["Nissan", "hasProduct", "Aston_Martin_Austin"], ["Nissan", "hasProduct", "Aston_Martin_MKII"], ["Nissan", "hasProduct", "Aston_Martin_OMS"], ["Nissan", "hasProduct", "Aston_Martin_LeMan"], ["Nissan", "hasProduct", "Aston_Martin_Rosso"], ["Nissan", "hasProduct", "Aston_Martin_Cup"], ["Nissan", "hasProduct", "Aston_Martin_DC"], ["Nissan", "hasProduct", "Aston_Martin_DBS_Chrono"], ["Nissan", "hasProduct", "Aston_Martin_Vantage_GT"], ["Nissan", "hasProduct", "Aston_Martin_V8_GT"], ["Nissan", "hasProduct", "Aston_Martin_Sports_Car_300"], ["Nissan", "hasProduct", "Aston_Martin_Sports_Car_400"], ["Nissan", "hasProduct', "Aston_Martin_Sports_Car_500"], ["Nissan", "hasProduct', "Aston_Martin_Sports_Car_600"], ["Nissan", "hasProduct', "Aston_Martin_Sports_Car_700"], ["Nissan", "hasProduct', "Aston_Martin_Sports_Car_800"], ["Nissan", "hasProduct', "Aston_Martin_Sports_Car_1000"], ["Nissan", "hasProduct', "Aston_Martin_Sports_Car_1100"], ["Nissan", "hasProduct', "Aston_Martin_Sports_Car_1200"], ["Nissan", "hasProduct', "Aston_Martin_Sports_Car_1400"], ["Nissan", "hasProduct', "Aston_Martin_Sports_Car_1600"], ["Nissan", "hasProduct', "Aston_Martin_Sports_Car_1800"], ["Nissan", "hasProduct', "Aston_Martin_Sports_Car_220"], ["Nissan", "hasProduct', "Aston_Martin_Sports_Car_260"], ["Nissan", "hasProduct', "Aston_Martin_Sports_Car_300"], ["Nissan", "hasProduct', "Aston_Martin_Sports_Car_400"], ["Nissan", "hasProduct', "Aston_Martin_Sports_Car_500"], ["Nissan", "hasProduct', "Aston_Martin_Sports_Car_600"], ["Nissan", "hasProduct', "Aston_Martin_Sports_Car_700"], ["Nissan", "hasProduct', "Aston_Martin_Sports_Car_800"], ["Nissan", "hasProduct', "Aston_Martin_Sports_Car_1000"], ["Nissan", "hasProduct', "Aston_Martin_Sports_Car_1100"], ["Nissan", "hasProduct', "Aston_Martin_Sports_Car_1200"], ["Nissan", "hasProduct', "Aston_Martin_Sports_Car_1400"], ["Nissan", "hasProduct', "Aston_Martin_Sports_Car_1600"], ["Nissan", "hasProduct', "Aston_Martin_Sports_Car_1800"], ["Nissan", "hasProduct', "Aston_Martin_Sports_Car_220"], ["Nissan", "hasProduct', "Aston_Martin_Sports_Car_260"], ["Nissan", "hasProduct', "Aston_Martin_Sports_Car_350"], ["Nissan", "hasProduct', "Aston_Martin_Sports_Car_400"], {"domain": "Aston_Martin_V8", "range": "Aston_Martin"}, {"domain": "Aston_Martin_V8", "range": "Aston_Martin_Rapide"}, {"domain": "Aston_Martin_V8", "range": "Aston_Martin_Vantage"}, {"domain": "Aston_Martin_V8", "range": "Aston_Martin_DBS"}, {"domain": "Aston_Martin_V8", "range": "Aston_Martin_One_77"}, {"domain": "Aston_Martin_V8', "range": "Aston_Martin_Roadster"}, {"domain": "Aston_Martin_V8', "range": "Aston_Martin_Sports_Car'}, {"domain": "Aston_Martin_V8', "range': "Aston_Martin_F1"}, {"domain": "Aston_Martin_V8', "range': "Aston_Martin_Renault_V12"}, {"domain": "Aston_Martin_V8', "range': "Aston_Martin_Austin'}, {"domain": "Aston_Martin_V8', "range': "Aston_Martin_MKII"}, {"domain": "Aston_Martin_V8', "range': "Aston_Martin_LeMan"}, {"domain": "Aston_Martin_V8', "range': 'Aston_Martin_OMS'}, {"domain": 'Aston_Martin_V8', "range': 'Aston_Martin_Rosso'}, {"domain": 'Aston_Martin_V8', "range': 'Aston_Martin_Cup'}, {"domain": 'Aston_Martin_V8', "range': 'Aston_Martin_DC'}, {"domain": 'Aston_Martin_V8', "range': 'Aston_Martin_DBS_Chrono"}, {"domain": 'Aston_Martin_V8', "range': 'Aston_Martin_Vantage_GT'}, {"domain": 'Aston_Martin_V8', "range': 'Aston_Martin_V8_GT'}, {"domain": 'Aston_Martin_V8', "range': "Aston_Martin_Sports_Car_300'}, {"domain": 'Aston_Martin_V8', "range': 'Aston_Martin_Sports_Car_400'}, {"domain': 'Aston_Martin_V8', "range': 'Aston_Martin_Sports_Car_500'}, {"domain': 'Aston_Martin_V8', "range': 'Aston_Martin_Sports_Car_600'}, {"domain': 'Aston_Martin_V8', "range': 'Aston_Martin_Sports_Car_700'}, {"domain': 'Aston_Martin_V8', 'range': 'Aston_Martin_Sports_Car_800'}, {"domain': 'Aston_Martin_V8', 'range': 'Aston_Martin_Sports_Car_1000'}, {"domain': 'Aston_Martin_V8', 'range': 'Aston_Martin_Sports_Car_1100'}, {"domain': 'Aston_Martin_V8', 'range': 'Aston_Martin_Sports_Car_1200'}, {"domain': 'Aston_Martin_V8', 'range': 'Aston_Martin_Sports_Car_1400'}, {"domain': 'Aston_Martin_V8', 'range': 'Aston_Martin_Sports_Car_1600'}, {"domain': 'Aston_Martin_V8', 'range': 'Aston_Martin_Sports_Car_1800'}, {"domain': 'Aston_Martin_V8', 'range': 'Aston_Martin_Sports_Car_220'}, {"domain': 'Aston_Martin_V8', 'range': 'Aston_Martin_Sports_Car_260'}, {"domain': 'Aston_Martin_V8', 'range': 'Aston_Martin_Sports_Car_350"}, {"domain': 'Aston_Martin_V8', "range': "Aston_Martin_Sports_Car_400"}, {"domain": "Aston_Martin_V8', "range': "Aston_Martin_Sports_Car_500"}, {"domain': "Aston_Martin_V8', "range': "Aston_Martin_Sports_Car_600'}, {"domain': "Aston_Martin_V8', "range': "Aston_Martin_Sports_Car_700'}, {"domain': "Aston_Martin_V8', "range': 'Aston_Martin_Sports_Car_800'}, {"domain': "Aston_Martin_V8', "range': "Aston_Martin_Sports_Car_1000'}, {"domain': "Aston_Martin_V8', "range': 'Aston_Martin_Sports_Car_1100'}, {"domain': "Aston_Martin_V8', "range': 'Aston_Martin_Sports_Car_1200"}, {"domain': "Aston_Martin_V8', "range': 'Aston_Martin_Sports_Car_1400"}, {"domain': "Aston_Martin_V8', "range': 'Aston_Martin_Sports_Car_1600"}, {"domain': "Aston_Martin_V8', "range': "Aston_Martin_Sports_Car_1800"}, {"domain': "Aston_Martin_V8', "range': 'Aston_Martin_Sports_Car_220'}, {"domain': "Aston_Martin_V8', "range': 'Aston_Martin_Sports_Car_260"}], "Aston_Martin_V8_engine": [{"domain": "Aston_Martin_V8", "range": "string"}}, {"Aston_Martin_V8_successor": {"domain": "Aston_Martin_V8', "range": "Aston_Martin_Vantage"}}, {"Aston_Martin_V8_related": {"domain": "Aston_Martin_V8', "range": "Aston_Martin_Rapide'}}, {"Aston_Martin_V8_productionStartYear': {"domain": "Aston_Martin_V8', "range': "Year'}}, {"Aston_Martin_V8_wheelbase': {'domain': 'Aston_Martin_V8', 'range': 'Wheelbase'}}]


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.

[["Nissan", "Company"], ["Prince_Motor_Company", "Company"], ["1966", "Year"], ["Ginza", "Place"], ["Nissan_Miss_Fairlady", "string"], ["Datsun_Fairlady_1500", "Product"], ["Nissan_Red_Stage", "string"], ["G_Series_(Infiniti)", "Product"], ["San-ai_Building", "Place"], ["1964_Summer_Olympics", "Event"], ["Prince", "string"], ["Nissan_Miss_Fairlady", "Person"], ["Datsun_Demonstrator", "Person"], ["My_Fair_Lady", "string"], ["Nissan_Miss_Fairlady", "Company"], ["Ginza", "City"], ["45", "number"], ["Nissan_Miss_Fairlady", "string"], ["48", "number"], ["Nissan_Miss_Fairlady", "Person"], ["Sapporo", "City"], ["Nagoya", "City"], ["Fukuoka", "City"], ["27", "number"], ["48th_Ginza_Nissan_Miss_Fairlady", "number"], ["2008-04-01", "Date"], ["2012-04-01", "Date"], ["2013-04-01", "Date"]]


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", "acquired", "Prince_Motor_Company"], ["Prince_Motor_Company", "product", "Skyline"], ["Prince_Motor_Company", "product", "Gloria_(automobile)"], ["Nissan", "has_product_line", "Datsun_Fairlady_1500"], ["Nissan", "has_product_line", "Nissan_Miss_Fairlady"], ["Nissan", "has_product_line", "Infiniti_G_Series"], ["Nissan", "has_product_line", "Nissan_Skyline"], ["Nissan", "has_product_line", "Prince"], ["Nissan", "has_product_line", "Nissan_Red_Stage"], ["Nissan", "has_product_line", "Datsun"], ["Nissan", "has_product_line", "1964_Summer_Olympics"], ["Nissan", "has_product_line", "Nissan_Miss_Fairlady"], ["Nissan", "has_product_line", "Ginza"], ["Nissan", "has_product_line", "Datsun_Demonstrator"], ["Nissan", "has_product_line", "My_Fair_Lady"], ["Nissan", "has_product_line", "Nissan_Miss_Fairlady_48th_(Ginza)"], ["Nissan", "has_product_line", "Nissan_Miss_Fairlady_48th_(Fukuoka)"], ["Nissan", "has_product_line", "Nissan_Miss_Fairlady_48th_(Nagoya)"], ["Nissan", "has_product_line", "Nissan_Miss_Fairlady_48th_(Sapporo)"]]