Nissan

Electric vehicles

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, Electric vehicles: Nissan introduced its firstbattery electric vehicle, theNissan Altraat theLos Angeles International Auto Showon 29 December 1997.Unveiled in 2009, the EV-11prototypeelectric carwas based on theNissan Tiida(Versa in North America), with the conventional gasoline engine replaced with anall-electric drivetrain. In 2010, Nissan introduced thefirst-generation LEAFas the first series produced, all-electric vehicle launched globally.As of 2014, the Nissan Leaf was the world's best selling highway-capable all-electric car ever.Global sales totaled 100,000 Leafs by mid January 2014, representing a 45 percentmarket shareof worldwide pure electric vehicles sold since 2010.Global Leaf sales passed the 200,000 unit milestone in December 2015, and the Leaf continued ranking as the all-time best selling all-electric car. Nissan's second all-electric vehicle, theNissan e-NV200, was announced in November 2013.Series production at the Nissan Plan inBarcelona,Spain, began on 7 May 2014.The e-NV200 commercial van is based on the Nissan Leaf. Nissan plans to launch two additional battery electric vehicles by March 2017. In June 2016, Nissan announced it will introduce its firstrange extendercar in Japan before March 2017. Theseriesplug-in hybridwill use a new hybrid system, dubbed e-Power, which debuted with the Nissan Gripzconceptcrossover showcased at the September 2015Frankfurt Auto Show.As of August 2016[update], Nissan electric vehicles were sold in 48 world markets.Nissan global electric vehicle sales passed 275,000 units in December 2016. In 2018 Nissan sold its battery unitAESCtoEnvisionin order to focus on the production of vehicles.The second-generationNissan Leafwas launched by Nissan in Japan in 2018. By December 2020, 10 years after its introduction, cumulative global deliveries had reached 500,000 Leaf cars. In February 2023, Nissan announced its intent to produce electric vehicles with solid-state batteries by 2028.In November 2023, Nissan announced its intention to build electric versions of the Qashqai and Juke at its plant in Britain.

[["Nissan_Leaf", "launch_date", "2009"], ["Nissan_Leaf", "total_production", "500000"], ["Nissan_Leaf", "parent_company", "Nissan"], ["Nissan_Leaf", "revenue", "275000"], ["Nissan_Leaf", "launch_date", "Japan"], ["Nissan_Leaf", "region", "\"48 world markets\""], ["Nissan_Leaf", "parent_company", "Nissan"], ["Nissan_Leaf", "revenue", "1000000"], ["Nissan_Leaf", "launch_date", "\"2018\""], ["Nissan_Leaf", "total_production", "200000"], ["Nissan_Leaf", "launch_date", "2018-12-31"], ["Nissan_Leaf", "parent_company", "Nissan"], ["Nissan_Leaf", "revenue", "500000"], ["Nissan_Leaf", "launch_date", "1997-12-29"], ["Nissan_Leaf", "total_production", "275000"], ["Nissan_Leaf", "parent_company", "Nissan"], ["Nissan_Leaf", "region", "Japan"], ["Nissan_Leaf", "launch_date", "2016-06-01"], ["Nissan_Leaf", "total_production", "100000"], ["Nissan_Leaf", "parent_company", "Nissan"], ["Nissan_Leaf", "region", "Japan"], ["Nissan_Leaf", "launch_date", "2014-05-07"], ["Nissan_Leaf", "total_production", "300000"], ["Nissan_Leaf", "parent_company", "Nissan"], ["Nissan_Leaf", "region", "Japan"], ["Nissan_Leaf", "launch_date", "2013-11-01"], ["Nissan_Leaf", "total_production", "150000"], ["Nissan_Leaf", "parent_company", "Nissan"], ["Nissan_Leaf", "region", "Japan"], ["Nissan_Leaf", "launch_date", "2017-03-01"], ["Nissan_Leaf", "total_production", "50000"], ["Nissan_Leaf", "parent_company", "Nissan"], ["Nissan_Leaf", "region", "Japan"], ["Nissan_Leaf", "launch_date", "2028-01-01"], ["Nissan_Leaf", "total_production", "400000"], ["Nissan_Leaf", "parent_company", "Nissan"], ["Nissan_Leaf", "region", "Japan"], ["Nissan_Leaf", "launch_date", "2023-11-01"], ["Nissan_Leaf", "total_production", "80000"], ["Nissan_Leaf", "parent_company", "Nissan"], ["Nissan_Leaf", "region", "United Kingdom"], ["Nissan_Leaf", "launch_date", "2023-02-01"]]


Nissan

Autonomous cars

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, Autonomous cars: In August 2013 Nissan announced its plans to launch severaldriverless carsby 2020. The company built a dedicated autonomous driving proving ground in Japan, completed in 2014. Nissan installed its autonomous car technology in aNissan Leafall-electric carfor demonstration purposes and was demonstrated at Nissan 360 test drive event held in California in August 2013.In September 2013, the Leaf fitted with the prototype Advanced Driver Assistance System was granted a license plate that allows it to drive on Japanese public roads. The testing car was used by Nissan engineers to evaluate how its in-house autonomous driving software performs in the real world. Time spent on public roads will help refine the car's software for fully automated driving.The autonomous Leaf was demonstrated on public roads for the first time at a media event held in Japan in November 2013. The Leaf drove on the Sagami Expressway inKanagawa Prefecture, near Tokyo. Nissan vice chairmanToshiyuki Shigaand the prefecture's governor, Yuji Kuroiwa, rode in the car during the test.

[["Nissan", "Company"], ["Autonomous_car", "Product"], ["Nissan_Leaf", "Product"], ["Japan", "Place"], ["2013-08-01", "Date"], ["Nissan", "Person"], ["2020", "Date"], ["Nissan_360", "Place"], ["Kanagawa_Prefecture", "Place"], ["Toshiyuki_Shiga", "Person"], ["Nissan", "Person"], ["Japan", "Country"], ["Autonomous_cars", "Product"], ["Nissan_Leaf", "Company"], ["2014", "Date"], ["Nissan", "Person"], ["Nissan_Leaf", "Product"], ["Japan", "Place"], ["Nissan", "Company"], ["2013-09-01", "Date"], ["Nissan", "Person"], ["Nissan_Leaf", "Product"], ["California", "Place"], ["Nissan", "Company"], ["Nissan_Leaf", "Product"], ["Japan", "Place"], ["Nissan", "Person"], ["Nissan_Leaf", "Product"], ["Sagami_Expressway", "Place"], ["Nissan", "Company"], ["Nissan_Leaf", "Product"], ["Nissan", "Person"], ["Nissan_Leaf", "Product"], ["Japan", "Place"], ["Yuji_Kuroiwa", "Person"], ["Nissan", "Company"], ["Nissan_Leaf", "Product"], ["Nissan", "Person"], ["Nissan_Leaf", "Product"], ["Japan", "Place"], ["2013-11-01", "Date"]]


Nissan

Autonomous cars

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, Autonomous cars: In August 2013 Nissan announced its plans to launch severaldriverless carsby 2020. The company built a dedicated autonomous driving proving ground in Japan, completed in 2014. Nissan installed its autonomous car technology in aNissan Leafall-electric carfor demonstration purposes and was demonstrated at Nissan 360 test drive event held in California in August 2013.In September 2013, the Leaf fitted with the prototype Advanced Driver Assistance System was granted a license plate that allows it to drive on Japanese public roads. The testing car was used by Nissan engineers to evaluate how its in-house autonomous driving software performs in the real world. Time spent on public roads will help refine the car's software for fully automated driving.The autonomous Leaf was demonstrated on public roads for the first time at a media event held in Japan in November 2013. The Leaf drove on the Sagami Expressway inKanagawa Prefecture, near Tokyo. Nissan vice chairmanToshiyuki Shigaand the prefecture's governor, Yuji Kuroiwa, rode in the car during the test.

[["Nissan", "has_product_line", "Autonomous_car"], ["Nissan", "occurred_on_date", "2013-08-01"], ["Nissan", "occurred_on_date", "2014-12-31"], ["Nissan", "occurred_on_date", "2013-11-01"], ["Nissan", "occurred_on_date", "2013-09-01"], ["Nissan", "has_product_line", "Nissan_Leaf"], ["Nissan", "occurred_on_date", "2013-08-31"], ["Nissan", "has_product_line", "Acura"], ["Nissan", "occurred_on_date", "2013-12-31"], ["Nissan", "has_product_line", "Infiniti"], ["Nissan", "occurred_on_date", "2013-10-01"], ["Nissan", "has_product_line", "Murano"], ["Nissan", "has_product_line", "Sentra"], ["Nissan", "has_product_line", "Skyline"], ["Nissan", "has_product_line", "Takuma"], ["Nissan", "has_product_line", "X-Trail"], ["Nissan", "has_product_line", "Z"], ["Nissan", "has_product_line", "Fairlady_Z"], ["Nissan", "has_product_line", "370Z"], ["Nissan", "has_product_line", "GTR"], ["Nissan", "has_product_line", "Patrol"], ["Nissan", "has_product_line", "Pickup_Truck"], ["Nissan", "has_product_line", "Van"], ["Nissan", "has_product_line", "Caravan"], ["Nissan", "has_product_line", "Cabstar"], ["Nissan", "has_product_line", "Fuga"], ["Nissan", "has_product_line", "Kicks"], ["Nissan", "has_product_line", "Leaf"], ["Nissan", "has_product_line", "Moco"], ["Nissan", "has_product_line", "Pulsar"], ["Nissan", "has_product_line", "Senzo"], ["Nissan", "has_product_line", "Silvia"], ["Nissan", "has_product_line", "Stanza"], ["Nissan", "has_product_line", "Terrano"], ["Nissan", "has_product_line", "Ultramax"], ["Nissan", "has_product_line", "Vanette"], ["Nissan", "has_product_line", "Auster"], ["Nissan", "has_product_line", "Bassant"], ["Nissan", "has_product_line", "Bedford"], ["Nissan", "has_product_line", "Caballero"], ["Nissan", "has_product_line", "Datsun"], {"Nissan", "occurred_on_date", "2013-11-23"}, {"Nissan", "has_product_line", "Auris"}, {"Nissan", "has_product_line", "Bluebird"], {"Nissan", "has_product_line", "Carina"}, {"Nissan", "has_product_line", "Cefiro"}, {"Nissan", "has_product_line", "Cherry"}, {"Nissan", "has_product_line", "Debick"}, {"Nissan", "has_product_line", "Dodge"}, {"Nissan", "has_product_line", "Fleetwood"}, {"Nissan", "has_product_line", "Homy"}, {"Nissan", "has_product_line", "Infiniti_FX"}, {"Nissan", "has_product_line", "Kadett"}, {"Nissan", "has_product_line", "Lafesta"}, {"Nissan", "has_product_line", "Luv'}, {"Nissan", "has_product_line", "Maxima"}, {"Nissan", "has_product_line", "Murano"}, {"Nissan", "has_product_line", "Pathfinder"], {"Nissan", "has_product_line", "Presage"}, {"Nissan", "has_product_line", "Primastar"}, {"Nissan", "has_product_line", "Qashqai"], {"Nissan", "has_product_line", "Rogue"], {"Nissan", "has_product_line", "Seneca"}, {"Nissan", "has_product_line", "Silvia"], {"Nissan", "has_product_line", "Skyline"}, {"Nissan", "has_product_line", "Solstice"}, {"Nissan", "has_product_line", "Terrano"], {"Nissan", "has_product_line", "Vanette"], {"Nissan", "has_product_line", "X-Trail"], {"Nissan", "has_product_line", "Z"], {"Nissan", "has_product_line", "Fairlady_Z"], {"Nissan", "has_product_line", "370Z"}, {"Nissan", "has_product_line", "GTR"], {"Nissan", "has_product_line", "Murcielago"}, {"Nissan", "has_product_line", "Pulsar"}, {"Nissan", "has_product_line", "Stanza"], {"Nissan", "has_product_line", "Ultramax"}, {"Nissan", "has_product_line", "Van"], {"Nissan", "has_product_line", "Caravan"}, {"Nissan", "has_product_line", "Bedford"}, {"Nissan", "has_product_line", "Bassant"}, {"Nissan", "has_product_line', "Auster"}, {"Nissan", "has_product_line', "Fuga"}, {"Nissan", "has_product_line', "Kicks"}, {"Nissan", "has_product_line', "Leaf"}, {"Nissan", "has_product_line', "Moco"}, {"Nissan", "has_product_line', "Senzo"}, {"Nissan", "has_product_line', "Terrano"}, {"Nissan", "has_product_line', "Ultramax"}, {"Nissan", "has_product_line', "Vanette"}, {"Nissan", "has_product_line', "Datsun"}, {"Nissan", "has_product_line', "Caballero"}, {"Nissan", "has_product_line', "Debick"}, {"Nissan", "has_product_line', "Cherry"}, {"Nissan", "has_product_line', "Fleetwood"}, {"Nissan", "has_product_line', "Homy"}, {"Nissan", "has_product_line', "Infiniti_FX"}, {"Nissan", "has_product_line', "Kadett"}, {"Nissan", "has_product_line', "Lafesta"}, {"Nissan", "has_product_line', "Luv"}, {"Nissan", "has_product_line', "Maxima"}, {"Nissan", "has_product_line', "Pathfinder"}, {"Nissan", "has_product_line', "Presage"}, {"Nissan", "has_product_line', "Primastar"}, {"Nissan", "has_product_line', "Qashqai"}, {"Nissan", "has_product_line', "Rogue"}, {"Nissan", "has_product_line', "Seneca"}, {"Nissan", "has_product_line', "Solstice"}, {"Nissan", "has_product_line', "Murcielago"}]


Nissan

Non-automotive products

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, Non-automotive products: Nissan has also had a number of ventures outside the automotive industry, most notably the Tu–Ka mobile phone service (est. 1994), which was sold to DDI and Japan Telecom (both now merged intoKDDI) in 1999. Nissan offers a subscription-basedtelematicsservice in select vehicles to drivers in Japan, calledCarWings. Nissan also ownsNissan Marine, a joint venture withTohatsuCorp that produces motors for smaller boats and other maritime equipment. Nissan also built solid rocket motors for orbital launch vehicles such as theLambda 4SandM-V.The aerospace and defense division of Nissan was sold toIHI Corporationin 2000.

[["Nissan", "Company"], ["Tu–Ka", "Product"], ["1999", "Date"], ["DDI", "Company"], ["Japan_Telecom", "Company"], ["KDDI", "Company"], ["Nissan_Marine", "Product"], ["Tohatsu_Corp", "Company"], ["CarWings", "Product"], ["Nissan", "Company"], ["Lambda_4", "Product"], ["M-V_(rocket)", "Product"], ["IHI_Corporation", "Company"], ["2000", "Date"]]


Nissan

Non-automotive products

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, Non-automotive products: Nissan has also had a number of ventures outside the automotive industry, most notably the Tu–Ka mobile phone service (est. 1994), which was sold to DDI and Japan Telecom (both now merged intoKDDI) in 1999. Nissan offers a subscription-basedtelematicsservice in select vehicles to drivers in Japan, calledCarWings. Nissan also ownsNissan Marine, a joint venture withTohatsuCorp that produces motors for smaller boats and other maritime equipment. Nissan also built solid rocket motors for orbital launch vehicles such as theLambda 4SandM-V.The aerospace and defense division of Nissan was sold toIHI Corporationin 2000.

[["Nissan", "non_automotive_products", "Tu–Ka"], ["Tu–Ka", "occurred_in_year", "1994"], ["Tu–Ka", "acquired", "DDI"], ["Tu–Ka", "acquired", "Japan_Telecom"], ["Nissan", "non_automotive_products", "CarWings"], ["Nissan", "non_automotive_products", "Nissan_Marine"], ["Nissan_Marine", "parent_company", "Tohatsu_Corp"], ["Nissan", "non_automotive_products", "Lambda_4"], ["Nissan", "non_automotive_products", "M-V_(rocket)"], ["Nissan", "aerospace_and_defense_division", "IHI_Corporation"], ["Nissan", "aerospace_and_defense_division", "2000"]]


Nissan

Marketing activities

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, Marketing activities: Nismois the motorsports division of Nissan, founded in 1984. Nismo cars have participated in theAll Japan Sports Prototype Championship,Super GT,IMSA GT Championship,World Sportscar Championship,FIA World Endurance Championship,British Touring Car Championship,Supercars ChampionshipandBlancpain GT Series. Also, they were featured at theWorld Series by Nissanfrom 1998 to 2004. Nissan sponsored theLos Angeles Opengolf tournament from 1987 to 2007. Beginning in 2015, Nissan became thenaming rightssponsor forNissan Stadium, the home of theTennessee TitansandTennessee State Universityfootball teams inNashville.Nissan also became the official sponsor of theHeisman TrophyandUEFA Champions League.Since 2019, Nissan has been the naming rights sponsor forNissan Arena, the home of theBrisbane Bulletsbasketball team andQueensland Firebirdsnetballteam inBrisbane, Australia.

[["Nissan", "Company"], ["Nismo", "Division"], ["Nissan", "Product"], ["All_Japan_Sports_Prototype_Championship", "Competition"], ["Nissan", "Company"], ["Nissan_Stadium", "SportsTeam"], ["Nissan_Stadium", "Location"], ["Tennessee_Titans", "SportsTeam"], ["Nissan", "Company"], ["Los_Angeles_Open", "SportsTeam"], ["2007", "Year"], ["Nissan", "Company"], ["Nissan_Arena", "SportsTeam"], ["Brisbane_Bullets", "SportsTeam"], ["Nissan", "Company"], ["Heisman_Trophy", "SportsTeam"], ["Nissan_Arena", "Location"], ["Queensland_Firebirds", "SportsTeam"], ["Nissan", "Company"], ["World_Series_by_Nissan", "Competition"], ["1998", "Year"], ["2004", "Year"], ["Super_GT", "Competition"], ["Nissan", "Company"], ["FIA_World_Endurance_Championship", "Competition"], ["IMSA_GT_Championship", "Competition"], ["Nissan", "Company"], ["British_Touring_Car_Championship", "Competition"], ["Nissan", "Company"], ["World_Sportscar_Championship", "Competition"], ["Nissan", "Company"], ["Nissan_Stadium", "SportsTeam"], ["Nissan", "Company"], ["Tennessee_State_University", "SportsTeam"], ["Nissan", "Company"], ["UEFA_Champions_League", "SportsTeam"]]


Nissan

Marketing activities

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, Marketing activities: Nismois the motorsports division of Nissan, founded in 1984. Nismo cars have participated in theAll Japan Sports Prototype Championship,Super GT,IMSA GT Championship,World Sportscar Championship,FIA World Endurance Championship,British Touring Car Championship,Supercars ChampionshipandBlancpain GT Series. Also, they were featured at theWorld Series by Nissanfrom 1998 to 2004. Nissan sponsored theLos Angeles Opengolf tournament from 1987 to 2007. Beginning in 2015, Nissan became thenaming rightssponsor forNissan Stadium, the home of theTennessee TitansandTennessee State Universityfootball teams inNashville.Nissan also became the official sponsor of theHeisman TrophyandUEFA Champions League.Since 2019, Nissan has been the naming rights sponsor forNissan Arena, the home of theBrisbane Bulletsbasketball team andQueensland Firebirdsnetballteam inBrisbane, Australia.

[["Nissan", "has_product_line", "Nissan_Sports_Car"], ["Nissan", "marketing", "Nismo"], ["Nissan", "part_of", "Nissan"], ["Nissan", "founded_by", "Nissan"], ["Nissan", "has_product_line", "Nissan_Leaf"], ["Nissan", "has_product_line", "Nissan_Murano"], ["Nissan", "has_product_line", "Nissan_X-Trail"], ["Nissan", "has_product_line", "Nissan_Kicks"], ["Nissan", "has_product_line", "Nissan_Sentra"], ["Nissan", "has_product_line", "Nissan_Titan"], ["Nissan", "has_product_line", "Nissan_Versa"], ["Nissan", "has_product_line", "Nissan_Almera"], ["Nissan", "has_product_line", "Nissan_Pulsar"], ["Nissan", "has_product_line", "Nissan_Kickbee"], ["Nissan", "has_product_line", "Nissan_Patrol"], ["Nissan", "has_product_line", "Nissan_Skyline"], ["Nissan", "has_product_line", "Nissan_Fuga"], ["Nissan", "has_product_line", "Nissan_Z"], ["Nissan", "has_product_line", "Nissan_GT-R"], ["Nissan", "has_product_line", "Nissan_Kickstart"], ["Nissan", "has_product_line", "Nissan_Micra"], ["Nissan", "has_product_line", "Nissan_Serena"], ["Nissan", "has_product_line", "Nissan_Townster"], ["Nissan", "has_product_line", "Nissan_370Z"], ["Nissan", "has_product_line", "Nissan_Pathfinder"], ["Nissan", "has_product_line", "Nissan_Frontier"], ["Nissan", "has_product_line", "Nissan_Xterra"], ["Nissan", "has_product_line", "Nissan_Rogue"], ["Nissan", "has_product_line", "Nissan_Latio"], ["Nissan", "has_product_line", "Nissan_Kicks"], ["Nissan", "has_product_line", "Nissan_Maxima"], ["Nissan", "has_product_line", "Nissan_Vanette"], ["Nissan", "has_product_line", "Nissan_Caravan"], ["Nissan", "has_product_line", "Nissan_Silvia"], ["Nissan", "has_product_line", "Nissan_KD"], ["Nissan", "has_product_line", "Nissan_KD-Stars"], ["Nissan", "has_product_line", "Nissan_Micra-K"], ["Nissan", "has_product_line", "Nissan_Pao"], ["Nissan", "has_product_line", "Nissan_Zeod"], ["Nissan", "has_product_line", "Nissan_ATLAS"], ["Nissan", "has_product_line", "Nissan_Altima"], {"Nissan", "part_of", "Nissan"}, {"Nissan", "has_product_line", "Nissan_Leaf"], {"Nissan", "has_product_line", "Nissan_Sentra"}, {"Nissan", "has_product_line", "Nissan_Titan"}, {"Nissan", "has_product_line", "Nissan_Versa"}, {"Nissan", "has_product_line", "Nissan_X-Trail"}, {"Nissan", "has_product_line", "Nissan_Almera"}, {"Nissan", "has_product_line", "Nissan_Pulsar"}, {"Nissan", "has_product_line", "Nissan_Kicks"}, {"Nissan", "has_product_line", "Nissan_Micra"}, {"Nissan", "has_product_line", "Nissan_Serena"}, {"Nissan", "has_product_line", "Nissan_Townster"}, {"Nissan", "has_product_line", "Nissan_370Z"}, {"Nissan", "has_product_line", "Nissan_Pathfinder"}, {"Nissan", "has_product_line", "Nissan_Frontier"}, {"Nissan", "has_product_line", "Nissan_Rogue"}, {"Nissan", "has_product_line", "Nissan_Latio"}, {"Nissan", "has_product_line", "Nissan_Maxima"}, {"Nissan", "has_product_line", "Nissan_Vanette"}, {"Nissan", "has_product_line", "Nissan_Caravan"}, {"Nissan", "has_product_line", "Nissan_Silvia"}, {"Nissan", "has_product_line", "Nissan_KD"}, {"Nissan", "has_product_line", "Nissan_KD-Stars"}, {"Nissan", "has_product_line", "Nissan_Micra-K"}, {"Nissan", "has_product_line", "Nissan_Pao"}, {"Nissan", "has_product_line", "Nissan_Zeod"}, {"Nissan", "has_product_line", "Nissan_ATLAS"}, {"Nissan", "has_product_line", "Nissan_Altima"}]


Nissan

Research and development

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, Research and development: Nissan's central researchis inside the Oppama Plant site,Yokosuka, which began its operation in 1961, at the former site ofImperial Japanese Navy's Airborne Squadron base. In 1982, Nissan's technical centers inSuginami, TokyoandTsurumi, Yokohamawere combined into one: Nissan Technical Center (NTC) inAtsugi, Kanagawa, at the foot ofMount Ōyamaof theTanzawa Mountains. At its 30th anniversary, in 2012, NTC employed 9,500 employees in product development, design, production engineering, and purchasing. Nissan Technical Center works closely with its overseas operations: Nissan Technical Center (NTC)/North America,NTC/Mexico, Nissan Design America,  NissanSilicon ValleyOffice,and Nissan Technical Centre Europe (NTCE). In 2007, the company opened Nissan Advanced Technology Center (NATC), near the NTC site. It works in close contact with the central research, the Silicon Valley office, the technical office near the Nissan headquarters in centralYokohama, and the overseas offices inDetroit, Silicon Valley, andMoscow. Nissan's test courses are inTochigi(two courses),YokosukaandHokkaido.

[["Nissan_Technical_Center", "Company"], ["Oppama_Plant", "Location"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["9,500", "number"], ["Nissan_Technical_Center", "Location"], ["Atsugi,_Kanagawa", "Location"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", "Company"], ["Nissan_Technical_Center", "Product"], ["Nissan_Technical_Center", 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Nissan

Research and development

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, Research and development: Nissan's central researchis inside the Oppama Plant site,Yokosuka, which began its operation in 1961, at the former site ofImperial Japanese Navy's Airborne Squadron base. In 1982, Nissan's technical centers inSuginami, TokyoandTsurumi, Yokohamawere combined into one: Nissan Technical Center (NTC) inAtsugi, Kanagawa, at the foot ofMount Ōyamaof theTanzawa Mountains. At its 30th anniversary, in 2012, NTC employed 9,500 employees in product development, design, production engineering, and purchasing. Nissan Technical Center works closely with its overseas operations: Nissan Technical Center (NTC)/North America,NTC/Mexico, Nissan Design America,  NissanSilicon ValleyOffice,and Nissan Technical Centre Europe (NTCE). In 2007, the company opened Nissan Advanced Technology Center (NATC), near the NTC site. It works in close contact with the central research, the Silicon Valley office, the technical office near the Nissan headquarters in centralYokohama, and the overseas offices inDetroit, Silicon Valley, andMoscow. Nissan's test courses are inTochigi(two courses),YokosukaandHokkaido.

[["Nissan_Technical_Center", "location", "Oppama_Plant"], ["Oppama_Plant", "city", "Yokosuka"], ["Nissan_Technical_Center", "founded_by", "Nissan"], ["Nissan_Technical_Center", "has_trademark", "Imperial_Japanese_Navy"], ["Nissan_Technical_Center", "established", "1961"], ["Nissan_Technical_Center", "has_trademark", "Nissan"], ["Nissan_Technical_Center", "location", "Atsugi,_Kanagawa"], ["Nissan_Technical_Center", "staff", "9500"], ["Nissan_Technical_Center", "has_trademark", "Tanzawa_Mountains"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Advanced_Technology_Center"], ["Nissan_Advanced_Technology_Center", "location", "Atsugi,_Kanagawa"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(North_America)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center/United_States"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center/Mexico"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Design_America"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Silicon_Valley_Office"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_Europe"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(United_States)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(Moscow)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(Japan)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(Thailand)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(China)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(United_Kingdom)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(Germany)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(France)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(Italy)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(Spain)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(Austria)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(Belgium)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(Denmark)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(Sweden)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(Canada)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(Singapore)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(South_Korea)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(Malaysia)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(Taiwan)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(Philippines)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(Brazil)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(Argentina)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(Indonesia)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(Hungary)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(Poland)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(Almería)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(Portugal)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(Romania)"], ["Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(Russia)"], {"Nissan_Technical_Center", "has_trademark", "Nissan_Technical_Center_(Venice)"}]


Nissan

Nissan Digital Hubs

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

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

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

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nissan, Nissan Digital Hubs: In mid-2018, Nissan launched its first of many planned software and information technology development centers inThiruvananthapuram, Kerala, India.

[["Nissan", "Company"], ["Thiruvananthapuram", "Location"], ["Nissan_Digital_Hubs", "Product"], ["2018-06-28", "Date"]]


Nissan

Nissan Digital Hubs

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 Digital Hubs: In mid-2018, Nissan launched its first of many planned software and information technology development centers inThiruvananthapuram, Kerala, India.

[["Nissan", "launchDate", "\"In mid-2018\""], ["Nissan", "location", "\"Thiruvananthapuram, Kerala, India\""], ["Nissan", "product", "Nissan_Digital_Hubs"]]


Nissan

Manufacturing locations

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, Manufacturing locations: Data extracted from Nissan's international corporate website.

[["Nissan", "Company"], ["Nissan", "Product"], ["Nissan", "Company"], ["\"Data extracted from Nissan's international corporate website\"", "string"]]


Nissan

Manufacturing locations

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, Manufacturing locations: Data extracted from Nissan's international corporate website.

[["Nissan", "manufacturing_locations", "\"Data extracted from Nissan's international corporate website\""]]