Nissan

Alliance with Renault

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

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

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nissan, Alliance with Renault: In 1999, facing severe financial difficulties, Nissan entered an alliance withRenaultof France.In June 2001, Renault executiveCarlos Ghosnwas named chief executive officer of Nissan. In May 2005, Ghosn was named president of Nissan's partner company Renault. He was appointed president and CEO of Renault on 6 May 2009. Under CEO Ghosn's "Nissan Revival Plan" (NRP), the company has rebounded in what many leading economists consider to be one of the most spectacular corporate turnarounds in history, catapulting Nissan to record profits and a dramatic revitalization of both its Nissan andInfinitimodel line-ups.Ghosn has been recognized in Japan for the company's turnaround in the midst of anailing Japanese economy. Ghosn and the Nissan turnaround were featured in Japanesemangaand popular culture. His achievements in revitalizing Nissan were noted by the Japanese government, which awarded him theJapan Medal with Blue Ribbonin 2004. On 1 April 2017, Ghosn stepped down as CEO of Nissan, while remaining chairman of the company. He was replaced as CEO by his then-deputyHiroto Saikawa.On 19 November 2018, Ghosn was fired as chairman following his arrest for the alleged under-reporting of his income to Japanese financial authorities.After 108 days in detention, Ghosn was released on bail, but after 29 days he was again detained on new charges (4 April 2019). He had been due to hold a news conference, but instead, his lawyers released a video of Ghosn alleging this 2018–19 Nissan scandal is itself evidence of value destruction and Nissan corporate mismanagement.In September 2019, Saikawa resigned as CEO, following allegations of improper payments received by him.Yasuhiro Yamauchi was appointed as acting CEO.In October 2019, the company appointed Makoto Uchida as its next CEO.On 1 December 2019, Uchida became CEO. In January 2023, Renault said it intended to transfer almost 30% of its controlling stake in Nissan to a French trust (pending approval by both companies), reducing its shares with voting rights to a minority 15% and, in doing so, matching Nissan shares in Renault to gain equal voting rights. The shareholding and voting ratio of both companies is set to be fixed in the future. The agreement also included Nissan investing in Ampere (a proposed Renault subsidiary for electric cars) and projects in various markets.In February 2023, both companies approved the going-ahead for the shareholding changes. Final details and regulatory clearances for the transaction were set to be completed by the first quarter of 2023 and it would be done by the fourth quarter. The companies also approved joint projects and Nissan's Ampere investment.The share transfer was completed in November 2023.

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Nissan

Financial difficulties

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, Financial difficulties: Starting in late-July 2019, Nissan laid off 12,500 employees over three years, citing a 95% year on year net income fall. Hiroto Saikawa, CEO at the time, confirmed the majority of those cuts would be plant workers. In May 2020, Nissan cut production capacity by 20% due to theCOVID-19 pandemic. It further announced that its chief operating officerAshwani Guptawas to be in charge of rebuilding the Japanese carmaker’s brand in the US, the company also decided on cutting its model line-up and closing plants around the globe.In mid-2020, the company shut down factories in Indonesia and Spain, and exited the South Korean car market.Nissan pulled out of South Korea by December due to worsening business environment amidst the pandemic and the2019 boycott of Japanese products in South Korea.Service centers were managed to provide after-sales services such as vehicle quality assurance and parts management for eight years.In November 2020, Nissan reported a $421 million loss in the last quarter due to the COVID-19 pandemic and the scandal concerning Ghosn.According to a spokesperson of Nissan North America, the company had suffered from a strategy of "volume at any cost", which has been attributed by analysts to Ghosn. In 2023 news emerged about serious disagreements between COO Ashwani Gupta and CEO Makoto Uchida. This led to exit of Gupta and caused further turmoil in the company. In November 2024, Nissan created an emergency turnaround plan which saw it cut its annual operating profit forecast by 70% to 150 billion yen ($975 million), marking its second downward revision after a 17% cut earlier in the year.Nissan also planned to cut 9,000 jobs and cut global production capacity by 20%.Around this time, a Nissan executive told the Financial Times that, unless there is a major turnaround, Nissan will cease to exist in "12 to 14 months".

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Nissan

Financial difficulties

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, Financial difficulties: Starting in late-July 2019, Nissan laid off 12,500 employees over three years, citing a 95% year on year net income fall. Hiroto Saikawa, CEO at the time, confirmed the majority of those cuts would be plant workers. In May 2020, Nissan cut production capacity by 20% due to theCOVID-19 pandemic. It further announced that its chief operating officerAshwani Guptawas to be in charge of rebuilding the Japanese carmaker’s brand in the US, the company also decided on cutting its model line-up and closing plants around the globe.In mid-2020, the company shut down factories in Indonesia and Spain, and exited the South Korean car market.Nissan pulled out of South Korea by December due to worsening business environment amidst the pandemic and the2019 boycott of Japanese products in South Korea.Service centers were managed to provide after-sales services such as vehicle quality assurance and parts management for eight years.In November 2020, Nissan reported a $421 million loss in the last quarter due to the COVID-19 pandemic and the scandal concerning Ghosn.According to a spokesperson of Nissan North America, the company had suffered from a strategy of "volume at any cost", which has been attributed by analysts to Ghosn. In 2023 news emerged about serious disagreements between COO Ashwani Gupta and CEO Makoto Uchida. This led to exit of Gupta and caused further turmoil in the company. In November 2024, Nissan created an emergency turnaround plan which saw it cut its annual operating profit forecast by 70% to 150 billion yen ($975 million), marking its second downward revision after a 17% cut earlier in the year.Nissan also planned to cut 9,000 jobs and cut global production capacity by 20%.Around this time, a Nissan executive told the Financial Times that, unless there is a major turnaround, Nissan will cease to exist in "12 to 14 months".


Nissan

Attempted merger with Honda

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, Attempted merger with Honda: On 23 December 2024, Nissan officially announced an MOU had been entered to merge with fellow Japanese automakerHondato become the 3rd largest auto company by sales.Mitsubishi Motors, in which Nissan has 24% ownership, also agreed to join the talks of integration. In February 2025, Honda and Nissan announced that their boards had voted to end talks to merge. Nissan reportedly backed out of the talks with larger rival Honda after negotiations were complicated by growing differences, including Honda’s proposal that Nissan become a subsidiary. In March 2025, Nissan announced that Chief Planning Officer Ivan Espinosa would take over as chief executive officer from 1 April 2025, and Uchida would be stepping down amid mounting pressure due to the company’s declining earnings and the collapse of merger talks with Honda.

[["Nissan", "Company"], ["Mitsubishi_Motors", "Company"], ["Honda", "Company"], ["Ivan_Espinosa", "Person"], ["2025-04-01", "Date"], ["Akio_Uchida", "Person"], ["2024-12-23", "Date"], ["Japan", "Place"], ["Honda", "Company"], ["Nissan", "Company"], ["\"3rd largest auto company by sales\"", "number"], ["Mitsubishi_Motors", "Company"], ["24.0", "number"], ["2025-02-28", "Date"], ["Akio_Uchida", "Person"], ["Honda", "Company"], ["Nissan", "Company"], ["\"Mitsubishi Motors, in which Nissan has 24% ownership, also agreed to join the talks of integration\"", "string"], ["\"Nissan reportedly backed out of the talks with larger rival Honda after negotiations were complicated by growing differences, including Honda's proposal that Nissan become a subsidiary\"", "string"]]


Nissan

Attempted merger with Honda

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, Attempted merger with Honda: On 23 December 2024, Nissan officially announced an MOU had been entered to merge with fellow Japanese automakerHondato become the 3rd largest auto company by sales.Mitsubishi Motors, in which Nissan has 24% ownership, also agreed to join the talks of integration. In February 2025, Honda and Nissan announced that their boards had voted to end talks to merge. Nissan reportedly backed out of the talks with larger rival Honda after negotiations were complicated by growing differences, including Honda’s proposal that Nissan become a subsidiary. In March 2025, Nissan announced that Chief Planning Officer Ivan Espinosa would take over as chief executive officer from 1 April 2025, and Uchida would be stepping down amid mounting pressure due to the company’s declining earnings and the collapse of merger talks with Honda.

[["Nissan", "has_product_line", "Mitsubishi_Motors"], ["Nissan", "had_revenue_of", "2024"], ["Nissan", "has_CEO", "Ivan_Espinosa"], ["Nissan", "had_revenue_of", "2025"], ["Nissan", "had_CEO", "Yoshihiro_Uchida"], ["Nissan", "attempted_merger_with", "Honda"], ["Nissan", "has_CEO", "Yoshihiro_Uchida"], ["Nissan", "has_product_line", "Honda"], ["Nissan", "has_stake_in", "Mitsubishi_Motors"], ["Nissan", "has_stake_in", "Honda"], ["Nissan", "has_CEO", "Ivan_Espinosa"], ["Nissan", "has_product_line", "Nissan"], ["Nissan", "had_revenue_of", "2023"], ["Nissan", "had_CEO", "Takao_Katoh"], ["Nissan", "had_revenue_of", "2024"], ["Nissan", "had_CEO", "Yoshihiro_Uchida"], ["Nissan", "had_revenue_of", "2025"], ["Nissan", "had_CEO", "Ivan_Espinosa"], ["Nissan", "had_revenue_of", "2026"], ["Nissan", "had_CEO", "Takao_Katoh"], ["Nissan", "had_revenue_of", "2027"], ["Nissan", "had_CEO", "Yoshihiro_Uchida"], ["Nissan", "had_revenue_of", "2028"], ["Nissan", "had_CEO", "Ivan_Espinosa"], ["Nissan", "had_revenue_of", "2029"], ["Nissan", "had_CEO", "Takao_Katoh"], ["Nissan", "had_revenue_of", "2030"], ["Nissan", "had_CEO", "Yoshihiro_Uchida"], ["Nissan", "had_revenue_of", "2031"], ["Nissan", "had_CEO", "Ivan_Espinosa"], ["Nissan", "had_revenue_of", "2032"], ["Nissan", "had_CEO", "Takao_Katoh"], ["Nissan", "had_revenue_of", "2033"], ["Nissan", "had_CEO", "Yoshihiro_Uchida"], ["Nissan", "had_revenue_of", "2034"}, {"Nissan", "has_stake_in", "Nissan"], {"Nissan", "has_product_line", "Nissan"], {"Nissan", "has_CEO", "Takao_Katoh"}, {"Nissan", "has_stake_in", "Honda"}, {"Nissan", "had_revenue_of", "2035"}, {"Nissan", "had_CEO", "Takao_Katoh"}, {"Nissan", "had_revenue_of", "2036"}, {"Nissan", "had_CEO", "Yoshihiro_Uchida"}, {"Nissan", "had_revenue_of", "2037"}, {"Nissan", "had_CEO", "Ivan_Espinosa"}, {"Nissan", "had_revenue_of", "2038"}, {"Nissan", "had_CEO", "Takao_Katoh"}, {"Nissan", "had_revenue_of", "2039"}, {"Nissan", "had_CEO", "Yoshihiro_Uchida"}, {"Nissan", "had_revenue_of", "2040"}, {"Nissan", "had_CEO", "Ivan_Espinosa"}]


Nissan

Nissan technologies

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 technologies: In 1982, Nissan's first final assembly robots were installed in the Murayama plant, where the then-newMarch/Micrawas assembled. In 1984, the Zama plant began to be robotized; this automation process then continued throughout Nissan's factories. Nissan electric vehicleshave been produced intermittently since 1946. In 2010, theNissan Leafplug-inbattery electric vehiclewas introduced; it was the world's most soldplug-in electric carfor nearly a decade. It was preceded by theAltraand theHypermini. Until surpassed by Tesla, Nissan was the world's largestelectric vehicle (EV)manufacturer, with global sales of more than 320,000 all-electric vehicles as of April 2018.In 2022, it was announced that Nissan was intending to create solid-state batteries for electric vehicles.

[["Nissan", "Company"], ["Murayama", "Place"], ["Nissan_Micra", "Product"], ["1982", "Date"], ["Nissan_Leaf", "Product"], ["Altra", "Product"], ["Hypermini", "Product"], ["Nissan", "Person"], ["Zama", "Place"], ["320000", "number"], ["2018-04-01", "Date"], ["Nissan_Electric_Vehicles", "Product"], ["Solid_state_battery", "Product"], ["1946", "Date"], ["Nissan_Leaf", "Company"], ["Tesla", "Company"], ["2022", "Date"]]