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.

Nissan

Q30923926 - Hiroto Saikawa: Japanese businessman, former president of Nissan

Nissan

Q356719 - Carlos Ghosn: French-Brazilian-Lebanese businessman

Q107618108 - Carlos Ghosn's Mad Dash Captivates Japan: "His Escape Was Like a Novel": None

Q118457363 - Carlos Ghosn: The Last Flight: tv movie/episiode

Q97600312 - Carlos Ghosn : le conseil d'administration de Renault refuse de payer ses indemnités de départ: None

Q116202634 - Fugitive: The Curious Case Of Carlos Ghosn: 2022 film directed by Lucy Blakstad

Nissan

Q59779657 - Yasuhiro Yamauchi: None
Q40042566 - Yasuhiro Yamauchi: None

Nissan

Q89784273 - Makoto Uchida: Japanese particle physicist

Q3489767 - Makoto Uchida: Japanese video game developer

Q70092183 - Makoto Uchida: Japanese businessman

Nissan

P5832 - political coalition: agreement for cooperation between different political parties

P114 - airline alliance: alliance the airline belongs to

Q6686 - Renault: automotive conglomerate

Q98584518 - Renault: French automobile manufacturer

Q453485 - Jean-Baptiste Regnault: French painter (1754–1829)

Q2636184 - Renault: agricultural machinery division of the car manufacturer, established in 1918

Nissan

Q29714 - Infiniti: Japanese luxury vehicle brand owned by Nissan

Q100952920 - zu infinitive: verb form in German

Q173663 - Red Bull Racing: Formula One racing team based in Milton Keynes, England

Q299531 - Los Angeles Open: tennis tournament

Nissan

Output: None of the candidates

Output: None of the candidates


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".

[
    "Q270195 - Nissan-lez-Enserune: commune in Hérault, France",
    "Q37455347 - Nissan: family name",
    "Q20165 - Nissan Motor Co. Ltd.: Japanese company",
    "Q958359 - Nissan: river in southern Sweden",
    "Q41156912 - Nisan: male given name (ניסן)"
]

[
    "Q30923926 - Hiroto Saikawa: Japanese businessman, former president of Nissan"
]

[
    "Q270195 - Nissan-lez-Enserune: commune in Hérault, France",
    "Q37455347 - Nissan: family name",
    "Q20165 - Nissan Motor Co. Ltd.: Japanese company",
    "Q958359 - Nissan: river in southern Sweden",
    "Q41156912 - Nisan: male given name (ניסן)"
]

[
    "Q57386028 - Financial difficulties, smoking habits, composition of the diet and birthweight in a population of pregnant women in the South West of England: article",
    "Q37349901 - Financial difficulties but not other types of recent negative life events show strong interactions with 5-HTTLPR genotype in the development of depressive symptoms: scientific article published on 03 May 2016",
    "Q47762806 - Financial difficulties in childhood and adult depression in Europe: scientific article",
    "Q93179516 - Financial difficulties in bipolar disorder part 1: longitudinal relationships with mental health: scientific article published on 16 November 2018",
    "Q101221320 - Financial Difficulties in Families of Hospitalized Children: scientific article published on 21 October 2020"
]

[
    "Q115670988 - Ashwani Gupta: Alliance Senior Vice-President, LCV Business - Renault-Nissan-Mitsubishi",
    "Q81767561 - Ashwani K Gupta: Indian American nephrologist",
    "Q85869819 - Ashwani Gupta: Indian engineer (born 1970)",
    "Q54611477 - Ashwani Gupta: mechanical engineer (born 1948)"
]

[
    "Q3121115 - Gustave Benjamin Boyer: Canadian politician",
    "Q131620299 - Gustave Boyer: None"
]

[
    "Q81068910 - COVID-19 pandemic: pandemic caused by SARS-CoV-2",
    "Q86694873 - COVID-19 pandemic in Africa: ongoing coronavirus pandemic in Africa",
    "Q84055514 - COVID-19 pandemic in India: ongoing COVID-19 viral pandemic in India",
    "Q83741704 - COVID-19 pandemic by country and territory: details regarding the COVID-19 pandemic by country and territory",
    "Q86597695 - COVID-19 pandemic in Brazil: ongoing coronavirus pandemic in Brazil"
]

Output: None of the candidates


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

Q270195 - Nissan-lez-Enserune: commune in Hérault, France

Q20165 - Nissan Motor Co. Ltd.: Japanese company

Q958359 - Nissan: river in southern Sweden

Q41156912 - Nisan: male given name (ניסן)

P2295 - net profit: private entity profit after accounting for all costs

P2283 - uses: item or concept used by the subject or in the operation (see also instrument [P1303] and armament [P520])

P2139 - total revenue: income gained by an organization during a given time frame. Not to be confused with fiscal revenue

Nissan

Nissan

Q87496002 - 0: digit zero (U+0030): Arabic-Latin digit

Q239 - City of Brussels: municipality and capital city of Belgium

Q87544564 - ０: fullwidth digit zero (U+FF10)

Nissan

P2770 - source of income: source of income of an organization or person

Nissan

Nissan

Nissan

Q84263196 - COVID-19: contagious disease caused by SARS-CoV-2

Q81068910 - COVID-19 pandemic: pandemic caused by SARS-CoV-2

Q82069695 - SARS-CoV-2: virus that causes COVID-19

Nissan

Q107442757 - 2020 Philippine census: census of the Philippines as of 1 May 2020

Q23766566 - 2020 United States Census: 24th national census of the United States, taken on April 1, 2020

Q99659280 - Second Nagorno-Karabakh War: armed conflict in South Caucasus

Nissan

Q124040144 - 2024: 2023 single by Playboi Carti

Q133576379 - 2024: 2025 song by Playboi Carti

Q995653 - 2024 Summer Olympics: games of the XXXIII Olympiad, in Paris, France

Nissan

P1476 - title: published name of a work, such as a newspaper article, a literary work, piece of music, a website, or a performance work

P793 - significant event: significant or notable events associated with the subject

P2561 - name: name the subject is known by; preferably use a more specific subproperty if available

P800 - notable work: notable scientific, artistic or literary work, or other work of significance among subject's works

P1317 - floruit: date when the person was known to be active or alive, when birth or death not documented

Nissan

Q86694873 - COVID-19 pandemic in Africa: ongoing coronavirus pandemic in Africa

Q84055514 - COVID-19 pandemic in India: ongoing COVID-19 viral pandemic in India

Q83741704 - COVID-19 pandemic by country and territory: details regarding the COVID-19 pandemic by country and territory

Q86597695 - COVID-19 pandemic in Brazil: ongoing coronavirus pandemic in Brazil

Nissan

Q49622 - 2023: calendar year
Q19241646 - 2023: natural number
Q226112 - 2023 Asaph: asteroid
Q2615604 - 20230 Blanchard: asteroid
Q773285 - 20234 Billgibson: asteroid

Nissan

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


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.

[
    "Q270195 - Nissan-lez-Enserune: commune in Hérault, France",
    "Q37455347 - Nissan: family name",
    "Q20165 - Nissan Motor Co. Ltd.: Japanese company",
    "Q958359 - Nissan: river in southern Sweden",
    "Q41156912 - Nisan: male given name (ניסן)"
]

[
    "Q36033 - Mitsubishi Motors: Japanese automotive manufacturer",
    "Q6883104 - Mitsubishi Mizushima FC: Japanese association football club",
    "Q1122373 - Ralliart: motorsports division of Mitsubishi Motors",
    "Q264136 - Badminton Horse Trials: equestrian triathalon",
    "Q183275 - Mitsubishi: group of autonomous, Japanese multinational companies"
]

[
    "Q9584 - Honda: manufacturer of automobiles, motorcycles, and power equipment",
    "Q14497179 - Honda: family name",
    "Q492312 - Hondarribia: municipality of Spain",
    "Q3259531 - Masaji Honda: Japanese botanist (1897-1984)",
    "Q19347129 - Honda: Japanese family name (本田)"
]

[
    "Q61481949 - Iván Espinosa de los Monteros: Spanish far-right politician",
    "Q28718036 - Iván Espinosa: None"
]

[
    "Q69307493 - April 1, 2025: date in Gregorian calendar"
]

[
    "Q69307392 - December 23, 2024: date in Gregorian calendar"
]

[
    "Q17 - Japan: island country in East Asia",
    "Q13989 - Honshu: largest island of Japan",
    "Q5287 - Japanese: language spoken in East Asia",
    "Q188712 - Empire of Japan: Empire in the Asia-Pacific region from 1868 to 1947",
    "Q205662 - Tokugawa shogunate: 1603–1868 Japanese military government"
]

[
    "Q69307461 - February 28, 2025: date in Gregorian calendar",
    "Q136213440 - 2025-02-28 Grizzlys Wolfsburg gegen Kölner Haie: None"
]

Output: None of the candidates


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

Q36033 - Mitsubishi Motors: Japanese automotive manufacturer

Q6883104 - Mitsubishi Mizushima FC: Japanese association football club

Q1122373 - Ralliart: motorsports division of Mitsubishi Motors

Q264136 - Badminton Horse Trials: equestrian triathalon

Q183275 - Mitsubishi: group of autonomous, Japanese multinational companies

Nissan

Nissan

P169 - chief executive officer: highest-ranking corporate officer appointed as the CEO within an organization

Q61481949 - Iván Espinosa de los Monteros: Spanish far-right politician

Nissan

Q135079650 - 2025: video game developed by Big Bros

Q3598057 - 20252 Eyjafjallajökull: asteroid

Nissan

Q8055958 - Yosh Uchida: American businessman

Q22129300 - Yoshihiro Uchida: Japanese poet

Nissan

Q9584 - Honda: manufacturer of automobiles, motorcycles, and power equipment

Q492312 - Hondarribia: municipality of Spain

Q3259531 - Masaji Honda: Japanese botanist (1897-1984)

Q19347129 - Honda: Japanese family name (本田)

Nissan

Nissan

Nissan

Nissan

Nissan

Q270195 - Nissan-lez-Enserune: commune in Hérault, France

Q20165 - Nissan Motor Co. Ltd.: Japanese company

Q958359 - Nissan: river in southern Sweden

Q41156912 - Nisan: male given name (ניסן)

Nissan

Q49622 - 2023: calendar year
Q19241646 - 2023: natural number
Q226112 - 2023 Asaph: asteroid
Q2615604 - 20230 Blanchard: asteroid
Q773285 - 20234 Billgibson: asteroid

Nissan

Nissan

Nissan

Q19605427 - 202686 Birkfellner: asteroid

Q6408992 - 202605 Shenchunshan: asteroid

Nissan

Q11187148 - 2027: natural number
Q12814 - 2027: calendar year
Q6408980 - 202778 Dmytria: asteroid
Q8215350 - 202736 Julietclare: asteroid
Q5684662 - 202787 Kestecher: asteroid

Nissan

Q6455593 - 202819 Carlosánchez: asteroid

Q1451505 - 2028 Summer Olympics: Games of the XXXIV Olympiad, in Los Angeles, USA

Nissan

Q63297473 - 202907 Meisenheimer: asteroid

Nissan

Q713789 - 2030s: next decade in the Gregorian calendar (2030-2039)

Q29389034 - 2030 Winter Olympics: 26th edition of the Winter Olympics in the French Alps, France

Nissan

Nissan

Q105834355 - 2032: The Legend of the Lost Future: 2007 studio album by Victor Argonov Project

Nissan

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


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.

[
    "Q270195 - Nissan-lez-Enserune: commune in Hérault, France",
    "Q37455347 - Nissan: family name",
    "Q20165 - Nissan Motor Co. Ltd.: Japanese company",
    "Q958359 - Nissan: river in southern Sweden",
    "Q41156912 - Nisan: male given name (ניסן)"
]

[
    "Q16877626 - Murayama: family name",
    "Q858008 - Murayama: city in Yamagata Prefecture, Japan",
    "Q21338793 - Siuiti Murayama: Japanese entomologist",
    "Q24091131 - Murayama: Japanese family name (村山)",
    "Q151054 - 3220 Murayama: asteroid"
]

[
    "Q738470 - Nissan Micra: car serie",
    "Q120752349 - Nissan Micra (K12): None",
    "Q11086837 - Nissan Micra C+C: None",
    "Q131339943 - Nissan Micra (K10): None",
    "Q131339964 - Nissan Micra (K11): None"
]

[
    "Q2436 - 1982: year",
    "Q12681710 - 1982: natural number",
    "Q32038868 - 1982: 2017 video game",
    "Q46934 - 1982 FIFA World Cup: 12th FIFA World Cup, held in Spain",
    "Q49103 - 1982 Lebanon War: 1982 war between Israel and forces in Lebanon"
]

[
    "Q911323 - Nissan Leaf: compact electric hatchback manufactured by Nissan",
    "Q38278176 - Nissan Leaf (first generation): battery electric compact car",
    "Q50398548 - Nissan Leaf Nismo Concept: None",
    "Q50398550 - Nissan Leaf Nismo Concept: None"
]

[
    "Q294532 - Altran: French company",
    "Q186360 - nurse: type of health care provider",
    "Q49631644 - Altra: island in Alstahaug, Norway",
    "Q444837 - Altranft: Quarter of Bad Freienwalde, Germany",
    "Q422416 - 17β-estradiol: primary female sex hormone"
]

[
    "Q270195 - Nissan-lez-Enserune: commune in Hérault, France",
    "Q37455347 - Nissan: family name",
    "Q20165 - Nissan Motor Co. Ltd.: Japanese company",
    "Q958359 - Nissan: river in southern Sweden",
    "Q41156912 - Nisan: male given name (ניסן)"
]

[
    "Q104833558 - Zama: surname",
    "Q145333 - Zama: city in Kanagawa prefecture, Japan",
    "Q8065628 - Zamarada: genus of insects",
    "Q1892445 - Zama Regia: battleground of the Battle of Zama, the final battle in the Second Punic War",
    "Q22931120 - Zama: 2016 film by Lucrecia Martel"
]

[
    "Q18258951 - Psmd14: protein-coding gene in the species Mus musculus",
    "Q18265089 - Tmem50a: protein-coding gene in the species Mus musculus",
    "Q18264227 - Gprc5c: protein-coding gene in the species Mus musculus",
    "Q18255181 - Tubb3: protein-coding gene in the species Mus musculus",
    "Q18300781 - Cdc42ep3: protein-coding gene in the species Mus musculus"
]

[
    "Q45920074 - April 1, 2018: Sunday in April 2018",
    "Q126014358 - 2018-04-01: short story by Liu Cixin",
    "Q131607292 - 2018-04-01 Arsenal Kyiv U-19 vs Dynamo Kyiv U-19: association football match at Kniazha Arena, Shchaslyve during 2017–18 Ukrainian Football Championship U-19 with score 0:5",
    "Q123967020 - 2018-04-01 China vs USA basketball match: None"
]

[
    "Q7041092 - Nissan electric vehicles: None"
]

[
    "Q7557794 - solid-state battery: battery that uses both solid electrodes and solid electrolytes",
    "Q123690213 - Solid-state battery: US patent 11522226",
    "Q122196712 - Solid-state battery: US patent 11424487",
    "Q119519854 - Solid state battery and solid state battery manufacturing method: US patent 11431030",
    "Q122416810 - Solid-state battery, battery pack, electric motor vehicle, power storage system, electric tool, and electronic device: US patent 11165095"
]

[
    "Q18610 - 1946: year",
    "Q12681697 - 1946: natural number",
    "Q53384720 - 1946-1947 one-year-period: period of about one year starting in 1946 and ending in 1947",
    "Q13143050 - 1946: Ethiopian year",
    "Q2616304 - 19462 Ulissedini: asteroid"
]

[
    "Q911323 - Nissan Leaf: compact electric hatchback manufactured by Nissan",
    "Q38278176 - Nissan Leaf (first generation): battery electric compact car",
    "Q50398548 - Nissan Leaf Nismo Concept: None",
    "Q50398550 - Nissan Leaf Nismo Concept: None"
]

[
    "Q478214 - Tesla, Inc.: American automotive, energy storage and solar power company",
    "Q163343 - tesla: SI unit of magnetic flux density",
    "Q1428953 - Tesla: American hard rock band",
    "Q37251206 - Tesla: family name",
    "Q9036 - Nikola Tesla: Serbian-American engineer and inventor (1856–1943)"
]

[
    "Q49625 - 2022: year",
    "Q19241645 - 2022: natural number",
    "Q110999040 - Russian invasion of Ukraine: ongoing military conflict in Eastern Europe since 2022",
    "Q108920739 - 2022 UCI Europe Tour: eighteenth season of the UCI Europe Tour",
    "Q110263921 - 2022 UCI World Ranking: None"
]

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


Nissan

Nissan technologies

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 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

P1056 - product or material produced: material or product, including services, produced or provided by an organization, industry, facility, or process

Q911323 - Nissan Leaf: compact electric hatchback manufactured by Nissan

Q38278176 - Nissan Leaf (first generation): battery electric compact car

Q50398548 - Nissan Leaf Nismo Concept: None

Q50398550 - Nissan Leaf Nismo Concept: None

Nissan_Leaf

Q38278176 - Nissan Leaf (first generation): battery electric compact car

Q50398548 - Nissan Leaf Nismo Concept: None

Q50398550 - Nissan Leaf Nismo Concept: None

P618 - source of energy: describes the source of energy an animated object (machine or animal) uses

Q7205234 - plug-in electric vehicle: type of electric vehicle

Q17022111 - plug-in electric vehicles in the Netherlands: overview of plug-in electric vehicles in the Netherlands

Q28154199 - plug-in electric vehicles in France: overview of plug-in electric vehicles in France

Q111949879 - Plug-in electric vehicles in Texas: None

Q65336937 - Plug-in electric vehicles in Iceland: None

Nissan_Leaf

P155 - follows: immediately prior item in a series of which the subject is a part, preferably use as qualifier of P179 [if the subject has replaced the preceding item, e.g. political offices, use "replaces" (P1365)]

P1365 - replaces: person, state or item replaced. Use "structure replaces" (P1398) for structures. Use "follows" (P155) if the previous item was not replaced or predecessor and successor are identical

P11951 - appears after phonological feature: this lexeme form appears only when it comes after another lexeme form with this phonological feature

P11953 - appears after lexeme form: this lexeme form appears only when it comes after this other specific lexeme form

Nissan_Leaf

Nissan

Q7041092 - Nissan electric vehicles: None

Nissan_electric_vehicles

Q7041092 - Nissan electric vehicles: None

Q53384720 - 1946-1947 one-year-period: period of about one year starting in 1946 and ending in 1947

Nissan

Nissan_Technologies

Q46934 - 1982 FIFA World Cup: 12th FIFA World Cup, held in Spain

Q49103 - 1982 Lebanon War: 1982 war between Israel and forces in Lebanon

Nissan_Technologies

Nissan_Technologies

Nissan_Technologies

Q523716 - 2010 United States Census: national census of the United States taken in 2010

Q92953148 - 2010 United States Census surname index: table of surnames with 100 or more occurrences in the 2010 US census

Q107569809 - 2010 U.S. Gazetteer Files: geographical census data for regions in the United States

Nissan_Technologies

P156 - followed by: immediately following item in a series of which the subject is a part, preferably use as qualifier of P179 [if the subject has been replaced, e.g. political offices, use "replaced by" (P1366)]

P1366 - replaced by: other person or item which continues the item by replacing it in its role. Use P156 ("followed by") if the item is not replaced nor identical, but adds to the series (e.g. books in a series).

P1398 - structure replaces: the item this building or structure replaced, at the same geographic location

Nissan

Q18258951 - Psmd14: protein-coding gene in the species Mus musculus

Q18265089 - Tmem50a: protein-coding gene in the species Mus musculus

Q18264227 - Gprc5c: protein-coding gene in the species Mus musculus

Q18255181 - Tubb3: protein-coding gene in the species Mus musculus

Q18300781 - Cdc42ep3: protein-coding gene in the species Mus musculus

Nissan

Q126014358 - 2018-04-01: short story by Liu Cixin

Q131607292 - 2018-04-01 Arsenal Kyiv U-19 vs Dynamo Kyiv U-19: association football match at Kniazha Arena, Shchaslyve during 2017–18 Ukrainian Football Championship U-19 with score 0:5

Q123967020 - 2018-04-01 China vs USA basketball match: None

Nissan

Q11086832 - Nissan March Box: None
Q738470 - Nissan Micra: car serie

Nissan

Q7557794 - solid-state battery: battery that uses both solid electrodes and solid electrolytes

Q123690213 - Solid-state battery: US patent 11522226

Q122196712 - Solid-state battery: US patent 11424487

Q119519854 - Solid state battery and solid state battery manufacturing method: US patent 11431030

Q122416810 - Solid-state battery, battery pack, electric motor vehicle, power storage system, electric tool, and electronic device: US patent 11165095

Nissan

P131 - located in the administrative territorial entity: the item is located on the territory of the following administrative entity. Use P276 for specifying locations that are non-administrative places and for items about events. Use P1382 if the item falls only partially into the administrative entity.

P276 - location: location of the object, structure or event; use P131 to indicate the containing administrative entity, P8138 for statistical entities, or P706 for geographic entities; use P7153 for locations associated with the object

P518 - applies to part: part, aspect, or form of the item to which the claim applies

P1071 - location of creation: place where the item was conceived or made; where applicable, location of final assembly

P2541 - operating area: geographic area or jurisdiction an organisation or industry operates in, serves, or has responsibility for

Q77076820 - World Flora Online: online flora for plants

Q362 - World War II: 1939–1945 global conflict

Q2 - Earth: third planet from the Sun in the Solar System

Q846596 - WorldCat: international union library catalog

Q189289 - World Aquatics: international sport governing body

Nissan

P749 - parent organization: parent organization of an organization, opposite of subsidiaries (P355)

P355 - child organization/unit: child organization/unit or subsidiary of a company or organization/unit; generally a fully owned separate corporation. Compare with "business division" (P199). Opposite of parent organization/unit (P749).

Q6686 - Renault: automotive conglomerate

Q98584518 - Renault: French automobile manufacturer

Q453485 - Jean-Baptiste Regnault: French painter (1754–1829)

Q2636184 - Renault: agricultural machinery division of the car manufacturer, established in 1918

Nissan

Q9544829 - Nissan Motor Indonesia: Indonesian subsidiary company

Nissan

Q110029696 - Nissan Motor Thailand Plant 2: motor vehicle assembly plant

Q110029689 - Nissan Motor Thailand Plant 1: motor vehicle assembly plant

Nissan

Nissan

Q7041008 - Nissan Motor India Private Limited: Indian subsidiary company

Nissan

Nissan

Nissan

Nissan

Nissan

Q1993716 - Nissan Motor Ibérica: Spanish subsidiary company

Nissan

Q1993724 - Nissan Motor Australia: former car manufacturer

Nissan

Nissan

Nissan

P31 - instance of: type to which this subject corresponds/belongs. Different from P279 (subclass of); for example: K2 is an instance of mountain; volcanoes form a subclass of mountains

P427 - taxonomic type: type genus or species: [in zoology:] nominal genus or species that is the name-bearing type of this nominal family or genus (or family-group ranked taxon or subgenus); type specimen that is the name-bearing type of this nominal species or subspecies

P2308 - class: qualifier to define a property constraint in combination with "property constraint" (P2302)

P105 - taxon rank: level in a taxonomic hierarchy

P279 - subclass of: this item is a subclass (subset) of that item; ALL instances of this item are instances of that item; different from P31 (instance of), e.g.: volcano is a subclass of mountain (and an instance of volcanic landform); Everest is an instance of mountain

Q6881511 - enterprise: organizational unit producing goods or services, which benefits from a certain degree of autonomy in decision-making, especially for the allocation of its current resources

Q783794 - company: legal entity representing an association of people, whether natural, legal or a mixture of both, with a specific objective

Q102538 - company: military unit size designation

Q1162163 - director: person who leads a particular area of a company or organization

Nissan

P36 - capital: seat of government of a country, province, state or other type of administrative territorial entity

P1376 - capital of: country, state, department, canton or other administrative division of which the municipality is the governmental seat

P2130 - capital cost: amount of money spent on building or producing an object

P6106 - uses capitalization for: lexical classes and subclasses of words as well as other uses in the language where the first letter is written as a capital letter

P2218 - net worth: totality of wealth possessed by a person

Q1490 - Tokyo: capital and largest city of Japan

Q7473516 - Tokyo: special wards in the eastern part of Tokyo Metropolis in Japan, that used to form a single city

Q41187 - Sony: Japanese multinational conglomerate corporation

Q7813796 - Tokyo: Masaharu Fukuyama song

Q7842 - University of Tokyo: national university in Tokyo, Japan

Nissan

P22 - father: male parent of the subject. For stepfather, use "stepparent" (P3448)

P25 - mother: female parent of the subject. For stepmother, use "stepparent" (P3448)

P171 - parent taxon: closest parent taxon of the taxon in question

P40 - child: subject has object as child. Do not use for stepchildren—use "relative" (P1038), qualified with "type of kinship" (P1039)

Q6686 - Renault: automotive conglomerate

Q98584518 - Renault: French automobile manufacturer

Q453485 - Jean-Baptiste Regnault: French painter (1754–1829)

Nissan

P176 - manufacturer: (main or final) manufacturer or producer of this product

P2079 - fabrication method: method, process or technique used to grow, cook, weave, build, assemble, manufacture the item

P272 - production company: company that produced this film, audio or performing arts work

Q1420 - car: motorized road vehicle designed to carry one to eight people rather than primarily goods

Q5386 - auto racing: motorsport involving the racing of cars for competition

Q188509 - suburb: human settlement that is part of or near to a larger city or town

Q3231690 - automobile model: industrial automobile model associated with a brand, defined usually from an engineering point of view by a combination of chassis/bodywork

Q10349745 - racing automobile driver: occupation driving an automobile in competition

Nissan

P3362 - operating income: private entity operating income

Nissan

Q861 - gallium: chemical element with symbol Ga and atomic number 31

Nissan

Q69269902 - January 3, 1933: date in Gregorian calendar

Nissan

P625 - coordinate location: geocoordinates of the subject. For Earth, please note that only the WGS84 geodetic datum is currently supported

P937 - work location: location where persons or organisations were actively participating in employment, business or other work

P3005 - valid in place: place where a statement is valid

Nissan

Nissan

Output: None of the candidates

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Nissan

Relationships with other car companies

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, Relationships with other car companies: In Australia, between 1989 and 1992,Nissan Australiashared models withFord Australiaunder a government-backed rationalisation scheme known as theButton Plan, with a version of theNissan Pintarabeing sold as theFord Corsairand a version of theFord Falconas theNissan Ute.A variant of the Nissan Patrol was sold as the Ford Maverick during the 1988–94 model years. In North America, Nissan partnered withFordfrom 1993 to 2002 to market the Ohio-builtMercury Villagerand theNissan Quest. The two minivans were virtually identical aside from cosmetic differences. In 2002, Nissan and Ford announced the discontinuation of the arrangement.In Europe, Nissan and Ford Europe partnered to produce theNissan Terrano IIand the badge-engineered Ford Maverick, a mid-size SUV produced at the Nissan Motor Ibérica S.A (NMISA) plant inBarcelona,Spain. The Maverick/Terrano II was a popular vehicle sold throughout Europe and Australasia. It was also sold in Japan as acaptive import, with the Nissan model marketed as the Nissan Mistral. Nissan licensed theVolkswagen Santana. Production began in 1984, at Nissan'sZama, Kanagawaplant,and ended in May 1990. From 1983 to 1987, Nissan cooperated with Alfa Romeo to build theArna.The goal was for Alfa to compete in the family hatchback market segment, and for Nissan to establish a foothold in the European market.After Alfa Romeo's takeover by Fiat, both the car and cooperation were discontinued. In Europe, General Motors (GM) and Nissan co-operated on theNissan Primastar, a light commercial vehicle. The high roof version is built in the NMISA plant inBarcelona, Spain; while the low roof version is built atVauxhall Motors/Opel's Luton plant inBedfordshire, UK. In 2013, GM announced its intentions to rebadge theNissan NV200commercial van as the 2015 model year Chevrolet City Express, to be introduced by the end of 2014.Holden, GM's Australian subsidiary, sold versions of theNissan Pulsaras theHolden Astrabetween 1984 and 1989. LDV Groupsold a badge-engineered light commercial vehicle version of theNissan Serenaas theLDV Cubfrom 1996 to 2001. The Nissan equivalent was marketed as the Nissan Vanette Cargo. In March 2023, Nissan andHondasigned a memorandum of understanding regarding a strategic partnership to collaborate on producing components for electric vehicles andartificial intelligencein automotive software platforms.The following year, talks between the two companies reportedly became deeper; media outlets suggested that a merger is being discussed.

[
    "Q270195 - Nissan-lez-Enserune: commune in Hérault, France",
    "Q37455347 - Nissan: family name",
    "Q20165 - Nissan Motor Co. Ltd.: Japanese company",
    "Q958359 - Nissan: river in southern Sweden",
    "Q41156912 - Nisan: male given name (ניסן)"
]

[
    "Q44294 - Ford: American multinational automotive company",
    "Q11247279 - Ford: family name",
    "Q20827633 - Ford: car brand owned by Ford Motor Company",
    "Q21021650 - Ford: female given name",
    "Q111045294 - Ford: badminton player"
]

[
    "Q1993801 - Nissan Pulsar: Japanese small car",
    "Q719974 - Nissan Sentra: car model from Nissan",
    "Q18432709 - Nissan Pulsar (Europe): None",
    "Q1569101 - Nissan Pulsar GTI-R: Hot hatch model from Nissan",
    "Q1141759 - Nissan Almera: Automobile By Nissan"
]

[
    "Q1030898 - Holden Astra: automobile"
]

[
    "Q1814517 - Nissan Vanette: Commercial vehicle produced by Nissan Motor Co."
]

[
    "Q524983 - LDV Cub: British van",
    "Q1814517 - Nissan Vanette: Commercial vehicle produced by Nissan Motor Co."
]

[
    "Q926124 - Nissan Qashqai: compact crossover SUV by Nissan",
    "Q116256985 - Nissan Qashqai (J10): motor vehicle",
    "Q10337031 - Nissan Qashqai +2: None",
    "Q50397087 - Nissan Qashqai Concept: None"
]

[
    "Q1141487 - Nissan Patrol: car model"
]

[
    "Q523437 - Nissan Terrano II: Compact SUV",
    "Q452035 - Nissan Terrano: SUV model from Nissan"
]

[
    "Q926124 - Nissan Qashqai: compact crossover SUV by Nissan",
    "Q116256985 - Nissan Qashqai (J10): motor vehicle",
    "Q10337031 - Nissan Qashqai +2: None",
    "Q50397087 - Nissan Qashqai Concept: None"
]

[
    "Q523437 - Nissan Terrano II: Compact SUV",
    "Q452035 - Nissan Terrano: SUV model from Nissan"
]

[
    "Q934829 - Nissan Serena: Minivan",
    "Q7651293 - Suzuki Landy: minivan"
]

[
    "Q1814517 - Nissan Vanette: Commercial vehicle produced by Nissan Motor Co."
]

[
    "Q1141487 - Nissan Patrol: car model"
]

[
    "Q270195 - Nissan-lez-Enserune: commune in Hérault, France",
    "Q37455347 - Nissan: family name",
    "Q20165 - Nissan Motor Co. Ltd.: Japanese company",
    "Q958359 - Nissan: river in southern Sweden",
    "Q41156912 - Nisan: male given name (ניסן)"
]

[
    "Q44294 - Ford: American multinational automotive company",
    "Q11247279 - Ford: family name",
    "Q20827633 - Ford: car brand owned by Ford Motor Company",
    "Q21021650 - Ford: female given name",
    "Q111045294 - Ford: badminton player"
]

[
    "Q523437 - Nissan Terrano II: Compact SUV",
    "Q452035 - Nissan Terrano: SUV model from Nissan"
]

[
    "Q934829 - Nissan Serena: Minivan",
    "Q7651293 - Suzuki Landy: minivan"
]

[
    "Q523437 - Nissan Terrano II: Compact SUV",
    "Q452035 - Nissan Terrano: SUV model from Nissan"
]

Output: None of the candidates

Output: None of the candidates


Nissan

Relationships with other car companies

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, Relationships with other car companies: In Australia, between 1989 and 1992,Nissan Australiashared models withFord Australiaunder a government-backed rationalisation scheme known as theButton Plan, with a version of theNissan Pintarabeing sold as theFord Corsairand a version of theFord Falconas theNissan Ute.A variant of the Nissan Patrol was sold as the Ford Maverick during the 1988–94 model years. In North America, Nissan partnered withFordfrom 1993 to 2002 to market the Ohio-builtMercury Villagerand theNissan Quest. The two minivans were virtually identical aside from cosmetic differences. In 2002, Nissan and Ford announced the discontinuation of the arrangement.In Europe, Nissan and Ford Europe partnered to produce theNissan Terrano IIand the badge-engineered Ford Maverick, a mid-size SUV produced at the Nissan Motor Ibérica S.A (NMISA) plant inBarcelona,Spain. The Maverick/Terrano II was a popular vehicle sold throughout Europe and Australasia. It was also sold in Japan as acaptive import, with the Nissan model marketed as the Nissan Mistral. Nissan licensed theVolkswagen Santana. Production began in 1984, at Nissan'sZama, Kanagawaplant,and ended in May 1990. From 1983 to 1987, Nissan cooperated with Alfa Romeo to build theArna.The goal was for Alfa to compete in the family hatchback market segment, and for Nissan to establish a foothold in the European market.After Alfa Romeo's takeover by Fiat, both the car and cooperation were discontinued. In Europe, General Motors (GM) and Nissan co-operated on theNissan Primastar, a light commercial vehicle. The high roof version is built in the NMISA plant inBarcelona, Spain; while the low roof version is built atVauxhall Motors/Opel's Luton plant inBedfordshire, UK. In 2013, GM announced its intentions to rebadge theNissan NV200commercial van as the 2015 model year Chevrolet City Express, to be introduced by the end of 2014.Holden, GM's Australian subsidiary, sold versions of theNissan Pulsaras theHolden Astrabetween 1984 and 1989. LDV Groupsold a badge-engineered light commercial vehicle version of theNissan Serenaas theLDV Cubfrom 1996 to 2001. The Nissan equivalent was marketed as the Nissan Vanette Cargo. In March 2023, Nissan andHondasigned a memorandum of understanding regarding a strategic partnership to collaborate on producing components for electric vehicles andartificial intelligencein automotive software platforms.The following year, talks between the two companies reportedly became deeper; media outlets suggested that a merger is being discussed.

Nissan

P102 - member of political party: the political party of which a person is or has been a member or otherwise affiliated

P1107 - proportion: to be used as a qualifier, value must be between 0 and 1

P1433 - published in: larger work that a given work was published in, like a journal, a website, a collection, a book or a music album

P478 - volume: volume of a book or music release in a collection/series or a published collection of journal issues in a serial publication

P59 - constellation: the area of the celestial sphere of which the subject is a part (from a scientific standpoint, not an astrological one)

Q270195 - Nissan-lez-Enserune: commune in Hérault, France

Q20165 - Nissan Motor Co. Ltd.: Japanese company

Q958359 - Nissan: river in southern Sweden

Q41156912 - Nisan: male given name (ניסן)

Nissan

P47 - shares border with: countries or administrative subdivisions, of equal level, that this item borders, either by land or water. A single common point is enough.

P4070 - identifier shared with: qualifier, to be used on external identifier IDs, indicating another Wikidata item is also matched to this ID

P9531 - identifier shared with lexeme: qualifier used to state which other lexemes are also matched to this identifier

P10756 - number of reblogs: number of reblogs, reposts, retweets, crossposts, or on-platform shares received on a social media post

Q9584 - Honda: manufacturer of automobiles, motorcycles, and power equipment

Q492312 - Hondarribia: municipality of Spain

Q3259531 - Masaji Honda: Japanese botanist (1897-1984)

Q19347129 - Honda: Japanese family name (本田)

Nissan

Q44294 - Ford: American multinational automotive company

Q20827633 - Ford: car brand owned by Ford Motor Company

Nissan

Q1993724 - Nissan Motor Australia: former car manufacturer

Nissan

Q1993716 - Nissan Motor Ibérica: Spanish subsidiary company

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Nissan

Q6686 - Renault: automotive conglomerate

Q98584518 - Renault: French automobile manufacturer

Q453485 - Jean-Baptiste Regnault: French painter (1754–1829)

Q2636184 - Renault: agricultural machinery division of the car manufacturer, established in 1918

Nissan

Q27530 - Mercedes-Benz Group: German automotive manufacturer

Q386598 - Daimler Buses: German bus and coach manufacturer

Q1157624 - Daimler Truck: German commercial vehicle manufacturer

Q27539 - Daimler Company: British motor vehicle manufacturer

Nissan

Q26823 - Abarth: Italian racing car maker

Q4573645 - Abarth 1000 GT Coupé: Alfa Romeo car

Q112220966 - Abarth T140: Concept racingcar

Q26234047 - Abarth 124 Spider: Sports car

Nissan

Q26921 - Alfa Romeo: Italian automotive manufacturer

Q124982078 - Alfa Romeo: Italian car brand

Q622489 - Alfa Romeo Racing: Formula One team

Q61467970 - Alfa Romeo: 1950-1951 period of the Alfa Romeo Formula One team

Q84562554 - Alfa Romeo: 1979-1985 period of the Alfa Romeo Racing Formula One team

Nissan

Q27597 - Fiat: automotive and aircraft brand manufacturing subsidiary of FCA Italy

Q940689 - Fiat: Italian multinational automotive manufacturing (1899-2014)

Q18042054 - TXLNG: protein-coding gene in the species Homo sapiens

Q324744 - Fiat G.91: attack aircraft family by Fiat

Nissan

Q35896 - Lancia: automobile manufacturing subsidiary of Stellantis

Nissan

Q40966 - Opel: German automobile manufacturer, subsidiary of Stellantis

Nissan

Q246 - Volkswagen: German automotive brand; manufacturing subsidiary of Volkswagen Group

Q156578 - Volkswagen Group: German automotive manufacturing conglomerate

Q1766743 - Volkswagen Foundation: private German charitable Foundation

Q1417237 - Volkswagen Bora: Saloon car related to the MK4 Golf.

Q155603 - Volkswagen Arena: football stadium in Wolfsburg, Germany

Nissan

Q482994 - album: grouping of album releases by an artist usually released at the same time with the same title and tracks but in different formats for consumption (digital, CD, LP)

Q23317 - Audi AG: German automotive manufacturing subsidiary of Volkswagen Group

Q7302866 - audio track: track on an audio release

Q128124 - audio engineer: engineer who operates recording, mixing, sound reproduction equipment

Nissan

Q6742 - Peugeot: French automotive brand of Stellantis

Q1624386 - Peugeot cycling team: French professional cycling team (1901–1986)

Q275623 - Groupe PSA: French automotive manufacturing corporation (1976-2021)

Nissan

Q6746 - Citroën: French automotive manufacturer

Q30024860 - Citroën: entry in Dictionnaire du patrimoine rennais

Nissan

Nissan

Q81965 - General Motors: American multinational automotive company

Q21683603 - General Motors TBM Avenger: torpedo bomber aircraft series by General Motors

Q5532064 - General Motors railway station: closed railway station in Dandenong South, Victoria, Australia

Q29281 - Holden: Australian subsidiary of General Motors, an automaker

Q30338742 - General Motors: Polish subsidary of General Motors

Nissan

Q27586 - Ferrari: Italian luxury sports car manufacturer; subsidiary of Ferrari N.V.

Q2254817 - Ferrari: 2003 film by Carlo Carlei

Q55516950 - Ferrari: 2018 single by Bebe Rexha

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