Nissan

Automotive products

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

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

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

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nissan, Automotive products: Nissan has produced an extensive range of mainstream cars and trucks, initially for domestic consumption but exported around the world since the 1950s. It also produced several memorablesports cars, including theDatsun Fairlady 1500, 1600 and 2000 Roadsters, theZ-car, an affordable sports car originally introduced in 1969; and theGT-R, a powerfulall-wheel-drivesports coupe. In 1985, Nissan created a tuning division,Nismo, for competition and performance development of such cars. One of Nismo's latest models is the370ZNismo. Nissan also sells a range ofkei cars, mainly as a joint venture with other Japanese manufacturers likeSuzukiorMitsubishi. Until 2013, Nissan rebadged kei cars built by other manufacturers. Beginning in 2013, Nissan and Mitsubishi shared the development of theNissan DAYZ / Mitsubishi eK Wagon series.Nissan also has shared model development of Japanese domestic cars with other manufacturers, particularlyMazda,Subaru,SuzukiandIsuzu. In China, Nissan produces cars in association with theDongfeng Motor Groupincluding the 2006Nissan Livina Geniss, the first in a range of a new worldwide family of medium-sized cars. In 2010, Nissan created another tuning division,IPL, this time for their premium/luxury brand Infiniti. In 2011, after Nissan released theNissan NV-Seriesin the United States, Canada, and Mexico, Nissan created a commercial sub-brand calledNissan Commercial Vehicleswhich focuses on commercial vans, pickup trucks, and fleet vehicles for the US, Canadian, and Mexican Markets.In 2013, Nissan launched theQashqaiSUV in South Africa, along with their new motorsport Qashqai Car Games.[when?]It is the same year when the Datsun brand was relaunched by Nissan after a 27-year hiatus. Nissan launched theirNissan Intelligent Mobilityvision in 2016 by revealing the IDS Concept at the2016 Geneva Motor Show.Most Nissan vehicles like theDayz,RogueandLeafare equipped with Nissan Intelligent Mobility technology. In 2018, Nissan launched the sixth-generationAltimaat the2018 New York Auto Show.

[
    "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)"
]

[
    "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"
]

[
    "Q1993846 - Nissan Rogue: automobile",
    "Q30896335 - Nissan Rogue Warrior Project: None"
]

[
    "Q11509845 - Nissan Dayz: None",
    "Q65247915 - Mitsubishi eK X: kei car model from Mitsubishi Motors"
]

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

[
    "Q132125644 - Nissan Altima: song by Doechii",
    "Q636791 - Nissan Altima: car model from Nissan",
    "Q135398086 - Nissan Altima: 2024 single by Doechii",
    "Q135398076 - Nissan Altima: vocal track by Doechii"
]

[
    "Q132125644 - Nissan Altima: song by Doechii",
    "Q636791 - Nissan Altima: car model from Nissan",
    "Q135398086 - Nissan Altima: 2024 single by Doechii",
    "Q135398076 - Nissan Altima: vocal track by Doechii"
]

[
    "Q50398328 - Nissan IDS Concept: None"
]

[
    "Q20165 - Nissan Motor Co. Ltd.: Japanese company"
]

[
    "Q1814517 - Nissan Vanette: Commercial vehicle produced by Nissan Motor Co.",
    "Q1172697 - Nissan Largo: Nissan passenger van"
]

[
    "Q1814517 - Nissan Vanette: Commercial vehicle produced by Nissan Motor Co.",
    "Q1172697 - Nissan Largo: Nissan passenger van"
]

[
    "Q499109 - Nissan GT-R: sports car model by Nissan",
    "Q18980952 - Nissan GT-R LM Nismo: Sports prototype racing car",
    "Q124250512 - Nissan GT-R variants: Japanese sports car variants",
    "Q50396786 - Nissan GT-R Proto: None",
    "Q50396993 - Nissan GT-R Concept: None"
]

[
    "Q1543585 - Nismo: motorsport and performance division of automobile manufacturer Nissan",
    "Q643635 - Nissan Skyline: car model from Nissan"
]

[
    "Q1543585 - Nismo: motorsport and performance division of automobile manufacturer Nissan",
    "Q643635 - Nissan Skyline: car model from Nissan"
]

[
    "Q776328 - Nissan Note: car model",
    "Q11509846 - Nissan Note (E12): Second-generation model of the Nissan Note",
    "Q114566698 - Nissan Note (E13): third-generation model of the Nissan Note",
    "Q113648974 - Nissan Note (E11): First generation of Nissan Note.",
    "Q107471625 - Nissan Note Aura: upmarket version of the third-generation Nissan Note"
]

[
    "Q11086832 - Nissan March Box: None",
    "Q738470 - Nissan Micra: car serie"
]

[
    "Q1993748 - Nissan Livina: MPV model from Nissan",
    "Q34001920 - Mitsubishi Xpander: compact MPV model by Mitsubishi Motors",
    "Q6151461 - Nissan Grand Livina: extended version of the first-generation Nissan Livina"
]

[
    "Q1993748 - Nissan Livina: MPV model from Nissan",
    "Q34001920 - Mitsubishi Xpander: compact MPV model by Mitsubishi Motors",
    "Q6151461 - Nissan Grand Livina: extended version of the first-generation Nissan Livina"
]

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

[
    "Q27543 - Datsun: automobile brand of Nissan"
]

[
    "Q27543 - Datsun: automobile brand of Nissan"
]

[
    "Q50398328 - Nissan IDS Concept: None"
]

[
    "Q776328 - Nissan Note: car model",
    "Q11509846 - Nissan Note (E12): Second-generation model of the Nissan Note",
    "Q114566698 - Nissan Note (E13): third-generation model of the Nissan Note",
    "Q113648974 - Nissan Note (E11): First generation of Nissan Note.",
    "Q107471625 - Nissan Note Aura: upmarket version of the third-generation Nissan Note"
]

[
    "Q11086832 - Nissan March Box: None",
    "Q738470 - Nissan Micra: car serie"
]

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

[
    "Q11509845 - Nissan Dayz: None",
    "Q65247915 - Mitsubishi eK X: kei car model from Mitsubishi Motors"
]

[
    "Q499109 - Nissan GT-R: sports car model by Nissan",
    "Q18980952 - Nissan GT-R LM Nismo: Sports prototype racing car",
    "Q124250512 - Nissan GT-R variants: Japanese sports car variants",
    "Q50396786 - Nissan GT-R Proto: None",
    "Q50396993 - Nissan GT-R Concept: None"
]

[
    "Q1993846 - Nissan Rogue: automobile",
    "Q30896335 - Nissan Rogue Warrior Project: None"
]

[
    "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"
]

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


Nissan

Trucks

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, Trucks: Nissan Cabstar (日産・キャブスターNissan Kyabusutā) is the name used in Japan for two lines of pickup trucks and light commercial vehicles sold by Nissan and built by UD Nissan Diesel, a Volvo AB company and by Renault-Nissan Alliance for the European market. The name originated with the 1968 Datsun Cabstar, but this was gradually changed over to "Nissan" badging in the early 1980s. The lighter range (1-1.5 tons) replaced the earlier Cabstar and Homer, while the heavier Caball and Clipper were replaced by the 2–4 ton range Atlas (日産・アトラスNissan Atorasu). The nameplate was first introduced in December 1981. The Cabstar is known also as the Nissan Cabstar, Renault Maxity and Samsung SV110 depending on the location. The range has been sold across the world. It shares its platform with the Nissan Caravan. TheNissan Titanwas introduced in 2004, as a full-size pickup truck produced for the North American market, the truck shares the stretched Nissan F-Alpha platform with theNissan ArmadaandInfiniti QX56SUVs. It was listed byEdmunds.comas the best full-size truck.The second-generation Titan was revealed at the2015 North American International Auto Showas a 2016 model year vehicle.

[
    "Q257673 - Nissan Atlas: car model",
    "Q10801450 - Nissan Cabstar: pickup trucks and light commercial vehicle",
    "Q109927063 - Nissan Atlas I: Nissan's light and medium-duty truck",
    "Q109927154 - Nissan Atlas II: Nissan's light and medium-duty truck"
]

[
    "Q257673 - Nissan Atlas: car model",
    "Q10801450 - Nissan Cabstar: pickup trucks and light commercial vehicle",
    "Q109927063 - Nissan Atlas I: Nissan's light and medium-duty truck",
    "Q109927154 - Nissan Atlas II: Nissan's light and medium-duty truck"
]

[
    "Q1241339 - UD Trucks: Japanese company"
]

[
    "Q163810 - AB Volvo: Swedish manufacturing company"
]

[
    "Q1820396 - Nissan Titan: Fullsize pickup truck"
]

[
    "Q1820396 - Nissan Titan: Fullsize pickup truck"
]

[
    "Q2014 - 2004: year",
    "Q906911 - 2004: natural number",
    "Q8558 - 2004 Summer Olympics: Games of the XXVIII Olympiad, in Athens, Greece",
    "Q28878253 - 2004 Moroccan census: census in Morocco",
    "Q109719080 - 2004 Indonesian People's Representative Council election: None"
]

[
    "Q11509796 - Nissan F-Alpha platform: None"
]

[
    "Q1993662 - Nissan Caravan: Urvan 2006",
    "Q109928179 - Nissan Caravan V: Nissan's light commercial van",
    "Q956356 - Nissan Elgrand: minivan produced by Nissan"
]

[
    "Q123966551 - 2015 North American International Auto Show: None"
]

[
    "Q25245 - 2016: year",
    "Q12681716 - 2016: natural number",
    "Q111610221 - 2016–2020 American Community Survey: American Community Survey on 5-year basis, data aggregated between 2016 and 2020",
    "Q32859555 - 2016 U.S. Gazetteer Files: geographical census data for regions in the United States",
    "Q33128519 - Australian Census 2016: 17th census of the population of Australia"
]

[
    "Q5339985 - edmunds.com: American automotive resource company"
]

[
    "Q611158 - Nissan Armada: car model"
]

[
    "Q611158 - Nissan Armada: car model"
]

[
    "Q19888560 - Infiniti QX80: luxury SUV model",
    "Q110268996 - Infiniti QX56: None",
    "Q1662414 - Infiniti QX: group of car models"
]

[
    "Q69295031 - December 31, 1981: date in Gregorian calendar"
]

[
    "Q2644 - 1968: year",
    "Q19241632 - 1968: natural number",
    "Q8429 - 1968 Summer Olympics: Games of the XIX Olympiad, in Mexico City, Mexico",
    "Q9642 - 1968 Winter Olympics: 10th edition of Winter Olympics, in Grenoble, France",
    "Q2812463 - 1968: 1968 studio album by France Gall"
]

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

Trucks

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, Trucks: Nissan Cabstar (日産・キャブスターNissan Kyabusutā) is the name used in Japan for two lines of pickup trucks and light commercial vehicles sold by Nissan and built by UD Nissan Diesel, a Volvo AB company and by Renault-Nissan Alliance for the European market. The name originated with the 1968 Datsun Cabstar, but this was gradually changed over to "Nissan" badging in the early 1980s. The lighter range (1-1.5 tons) replaced the earlier Cabstar and Homer, while the heavier Caball and Clipper were replaced by the 2–4 ton range Atlas (日産・アトラスNissan Atorasu). The nameplate was first introduced in December 1981. The Cabstar is known also as the Nissan Cabstar, Renault Maxity and Samsung SV110 depending on the location. The range has been sold across the world. It shares its platform with the Nissan Caravan. TheNissan Titanwas introduced in 2004, as a full-size pickup truck produced for the North American market, the truck shares the stretched Nissan F-Alpha platform with theNissan ArmadaandInfiniti QX56SUVs. It was listed byEdmunds.comas the best full-size truck.The second-generation Titan was revealed at the2015 North American International Auto Showas a 2016 model year vehicle.

Nissan_Cabstar

Q10801450 - Nissan Cabstar: pickup trucks and light commercial vehicle

Q109927063 - Nissan Atlas I: Nissan's light and medium-duty truck

Q109927154 - Nissan Atlas II: Nissan's light and medium-duty truck

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

Q109928179 - Nissan Caravan V: Nissan's light commercial van

Q956356 - Nissan Elgrand: minivan produced by Nissan

Nissan_Cabstar

Q1820396 - Nissan Titan: Fullsize pickup truck

Nissan_Titan

Q1820396 - Nissan Titan: Fullsize pickup truck

Nissan_Titan

Q19888560 - Infiniti QX80: luxury SUV model

Q1662414 - Infiniti QX: group of car models

Nissan_Titan

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

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

P2598 - serial number: an identifier for a specific object among the same product. Not a product code or model number

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_Titan

Q8558 - 2004 Summer Olympics: Games of the XXVIII Olympiad, in Athens, Greece

Q28878253 - 2004 Moroccan census: census in Morocco

Q109719080 - 2004 Indonesian People's Representative Council election: None

Nissan_Titan

P400 - platform: platform for which a work was developed or released, or the specific platform version of a software product

P1103 - number of platform tracks: number of tracks served by the platforms at a railway station

P5595 - number of platform faces: number of continuous platform edges facing a track or vehicle guideway in a station (often, but not always, the same as P1103 and the number of platform numbers). See property talk page for illustrated samples

P707 - spacecraft bus: general model on which multiple-production spacecraft is based

P13349 - Platform for Taiwan Religion and Folk Culture identifier: temple that is listed in the Platform for Taiwan Religion and Folk Culture ID

Q11509796 - Nissan F-Alpha platform: None

Nissan_Titan

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).

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

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

Nissan_Titan

Q63758826 - 2015-2021 term (departments of France): None

Q53386085 - 2015-2016 one-year-period: period of about one year starting in 2015 and ending in 2016

Q114717231 - World Economic Forum Annual Meeting 2015: None

Nissan_Titan

P408 - software engine: software engine employed by the subject item

P516 - powered by: equipment or engine used by the subject to convert a source or energy into mechanical energy

P10271 - Engineer's Line Reference: code for a railway line in Britain owned by Network Rail

P631 - structural engineer: person, group or organisation responsible for the structural engineering of a building or structure

P1002 - engine configuration: configuration of an engine's cylinders

Q341174 - V8: piston engine with eight cylinders in vee configuration

Nissan_Titan

P1060 - disease transmission process: process by which a pathogen is transmitted, equivalent to "transmitted by" in the relation ontology http://purl.obolibrary.org/obo/RO_0002451

Q843592 - automatic transmission: type of motor vehicle transmission that automatically changes gear ratio as the vehicle moves

Q122387567 - Automatic transmission: US patent 11199246

Q122663214 - Automatic transmission: US patent 11525488

Q122206551 - Automatic transmission: US patent 11306792

Q122326473 - Automatic transmission: US patent 11204098

Nissan_Titan

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

P2094 - competition class: official classification by a regulating body under which the subject (events, teams, participants, or equipment) qualifies for inclusion

P7174 - school class: (qualifier) class of a given year/time period to which the person belonged in an educational institution

P2676 - rating certificate ID: reference certificate identifier of content rating decisions

P289 - vessel class: series of vessels or other watercraft built to the same design of which this vessel is a member

Nissan_Titan

Q30 - United States: country located primarily in North America

Q637413 - United States Census Bureau: U.S. agency responsible for the census and related statistics

Q11703 - United States Virgin Islands: unincorporated territory of the United States of America

Q4917 - United States dollar: official currency of the United States

Q214867 - National Gallery of Art: national art museum in Washington, D.C.

Nissan_Titan

Q12439 - Detroit: city in and county seat of Wayne County, State of Michigan, United States

Q1201549 - Detroit Institute of Arts: art museum in Detroit, Michigan

Q271880 - Detroit Lions: National Football League franchise in Detroit, Michigan

Q21246348 - Detroit: Become Human: 2018 adventure video game developed by Quantic Dream

Q650855 - Detroit Tigers: baseball team and Major League Baseball franchise in Detroit, Michigan, United States of America

Nissan_Titan

Q1465915 - Nissan Navara: Pickup truck model from Nissan

Q105312001 - Nissan Frontier: pickup truck model from Nissan

Q134119910 - Nissan Yuanye Z9: mid-size pickup truck

Q107315603 - Nissan Navara (D23): Third-generation model of the Nissan Navara

Nissan_Titan

P577 - publication date: date or point in time when a work was first published or released

P571 - inception: time when an entity begins to exist; for date of official opening use P1619

P1619 - date of official opening: date or point in time a place, organization, or event officially opened

P729 - service entry: date or point in time on which a piece or class of equipment entered operational service

P619 - UTC date of spacecraft launch: date of spacecraft launch in UTC

Q69305786 - October 25, 2004: date in Gregorian calendar

Nissan_Titan

Nissan_Titan

Nissan_Titan

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_Titan

Q929804 - N/A: abbreviation for “not applicable”

Q11207061 - Nial J. Wheate: Australian chemist

Q3016180 - INS Rajali: naval air station in India

Q795410 - naturally aspirated engine: internal combustion engine that relies on atmospheric pressure for air intake without a supercharger or turbocharger

Q16065540 - Jane Johnson: American enslaved person

Nissan_Titan

Q10397412 - 8: 2005 studio album by Arvingarna

Nissan_Titan

P1549 - demonym: name for people or things associated with a given place, usually based off the place name

P6271 - demonym of: item that this sense is a demonym of

Nissan_Titan

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).

Nissan_Titan

P167 - structure replaced by: the item which replaced this building or structure, at the same geographic location

P694 - replaced synonym (for nom. nov.): previously published name on which a replacement name (avowed substitute, nomen novum) is based

P12764 - replaced synonym of: taxon item which replaced this taxon name (now only a synonym) - inverse property of replaced synonym (for nom. nov.) (P694)

Nissan_Titan

Q215601 - pickup truck: light-duty truck with an enclosed cab and an open cargo area

Q7190940 - Pickup Truck Racing: motor vehicle

Q114518440 - Pickup Trucks: episode of Modern Marvels (S9 E43)

Q7190944 - pickup truck racing: auto racing with modified pickup trucks

Nissan_Titan

P3039 - wheelbase: horizontal distance between the centers of the front and rear wheels

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

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

Electric vehicles

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

[
    "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"
]

[
    "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)"
]

[
    "Q26899 - Aesculus hippocastanum: species of plant",
    "Q2882860 - Aeschynomene: genus of plants",
    "Q1635829 - Aeschynanthus: genus of plants",
    "Q158752 - Aesculus: genus of plants",
    "Q180003 - American National Standards Institute: American standards development organization"
]

[
    "Q55610853 - EnVision: proposed mission to Venus",
    "Q11967616 - Envision: album by Parish",
    "Q97379658 - enVision: molecular biochemical technique",
    "Q410387 - monoethanolamine: chemical compound",
    "Q60190121 - Envisioning Global LGBT Human Rights : (Neo)colonialism, Neoliberalism, Resistance and Hope: non-fiction book"
]

[
    "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"
]

[
    "Q1996 - 2009: year",
    "Q51134 - 2009: natural number",
    "Q109682732 - 2009 Indonesian People's Representative Council election: 2009 election in Indonesia",
    "Q53385935 - 2009-2010 one-year-period: period of about one year starting in 2009 and ending in 2010",
    "Q212197 - 2009–10 UCI Europe Tour: None"
]

[
    "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)"
]

[
    "Q18397951 - Nissan e-NV200: light commercial vehicle",
    "Q1394779 - Nissan NV200: light commercial vehicle",
    "Q65269470 - Nissan e-NV200: None"
]

[
    "Q50398292 - Nissan Gripz Concept: None"
]

[
    "Q57583 - International Motor Show Germany: one of the world's largest motor shows"
]

[
    "Q21034802 - September 15, 2015: Tuesday in September 2015",
    "Q123750511 - Real Madrid v Shakhtar Donetsk, 15 September 2015: association football match at Santiago Bernabéu Stadium during 2015–16 UEFA Champions League with score 4:0"
]

[
    "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"
]

[
    "Q25291 - 2018: year",
    "Q12681715 - 2018: natural number",
    "Q9680 - 2018 Winter Olympics: 23rd edition of Winter Olympics, in Pyeongchang, South Korea",
    "Q170645 - 2018 FIFA World Cup: 21st FIFA World Cup, held in Russia",
    "Q47517245 - 2018 UCI World Ranking: None"
]

[
    "Q25418069 - 500,000: natural number",
    "Q4386118 - 500000 rubles: None",
    "Q4386079 - 5000000 rubles: None",
    "Q4639535 - 5,000,000: album by Dread Zeppelin",
    "Q130113239 - 500000 (Price Discovery))) (A Study on Price Discovery in KOSDAQ50 Index Spot and Futures Markets): scholarly article published in 2005"
]

[
    "Q19241651 - 2028: natural number",
    "Q12809 - 2028: calendar year",
    "Q6455593 - 202819 Carlosánchez: asteroid",
    "Q5686070 - 202806 Sierrastars: asteroid",
    "Q1451505 - 2028 Summer Olympics: Games of the XXXIV Olympiad, in Los Angeles, USA"
]

[
    "Q113366312 - Nissan Altra: electric car model",
    "Q1260338 - Nissan R'nessa: car model"
]

[
    "Q69301975 - December 29, 1997: date in Gregorian calendar"
]

[
    "Q145 - United Kingdom: country in north-west Europe",
    "Q174193 - United Kingdom of Great Britain and Ireland: historical sovereign state (1801–1922)",
    "Q7979 - British English: forms of the English language used in Britain",
    "Q161885 - Kingdom of Great Britain: constitutional monarchy in Western Europe (1707–1800)",
    "Q11010 - Parliament of the United Kingdom: supreme legislative body of the United Kingdom"
]

[
    "Q69306970 - November 1, 2023: date in Gregorian calendar",
    "Q131379942 - 2023-11-01 Handball Thüringer HC - HSG Bensheim/Auerbach: None"
]

[
    "Q3060531 - EuroVelo 1 - Atlantic Coast Route: European cycling route",
    "Q869924 - EuroVelo 15 - Rhine Cycle Route: European cycling route",
    "Q103128738 - EuroVelo 19 - Meuse Cycle Route: european cycling route",
    "Q1289628 - EuroVelo 13 - Iron Curtain Trail: cycling route in Europe",
    "Q103159272 - EuroVelo 14 - Waters of Central Europe: Еuropean cycling route"
]

[
    "Q530013 - Nissan Tiida: car model",
    "Q719974 - Nissan Sentra: car model from Nissan",
    "Q119845666 - Nissan Tiida (C11): None",
    "Q119575950 - Renault Symbol II: None"
]

[
    "Q530013 - Nissan Tiida: car model",
    "Q719974 - Nissan Sentra: car model from Nissan",
    "Q119845666 - Nissan Tiida (C11): None",
    "Q119575950 - Renault Symbol II: None"
]

[
    "Q29 - Spain: country in southwestern Europe with territories in Africa",
    "Q16932431 - Spain: family name",
    "Q42267 - Spain men's national football team: men's national association football team representing Spain",
    "Q178038 - Second Spanish Republic: political regime established in Spain between April 14, 1931 and April 1, 1939",
    "Q170603 - New Spain: viceroyalty of the Spanish Empire (1535-1821)"
]

[
    "Q69306696 - February 1, 2023: date in Gregorian calendar",
    "Q123667718 - 2023-02-01 TAU Castelló vs Gipuzkoa Basket: None",
    "Q123688570 - 2023-02-01 Handball Thüringer HC - HSG Bensheim/Auerbach: None"
]

[
    "Q57397044 - December 31, 2020: date in the Gregorian calendar"
]

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

Electric vehicles

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

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

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nissan, Electric vehicles: Nissan introduced its firstbattery electric vehicle, theNissan Altraat theLos Angeles International Auto Showon 29 December 1997.Unveiled in 2009, the EV-11prototypeelectric carwas based on theNissan Tiida(Versa in North America), with the conventional gasoline engine replaced with anall-electric drivetrain. In 2010, Nissan introduced thefirst-generation LEAFas the first series produced, all-electric vehicle launched globally.As of 2014, the Nissan Leaf was the world's best selling highway-capable all-electric car ever.Global sales totaled 100,000 Leafs by mid January 2014, representing a 45 percentmarket shareof worldwide pure electric vehicles sold since 2010.Global Leaf sales passed the 200,000 unit milestone in December 2015, and the Leaf continued ranking as the all-time best selling all-electric car. Nissan's second all-electric vehicle, theNissan e-NV200, was announced in November 2013.Series production at the Nissan Plan inBarcelona,Spain, began on 7 May 2014.The e-NV200 commercial van is based on the Nissan Leaf. Nissan plans to launch two additional battery electric vehicles by March 2017. In June 2016, Nissan announced it will introduce its firstrange extendercar in Japan before March 2017. Theseriesplug-in hybridwill use a new hybrid system, dubbed e-Power, which debuted with the Nissan Gripzconceptcrossover showcased at the September 2015Frankfurt Auto Show.As of August 2016[update], Nissan electric vehicles were sold in 48 world markets.Nissan global electric vehicle sales passed 275,000 units in December 2016. In 2018 Nissan sold its battery unitAESCtoEnvisionin order to focus on the production of vehicles.The second-generationNissan Leafwas launched by Nissan in Japan in 2018. By December 2020, 10 years after its introduction, cumulative global deliveries had reached 500,000 Leaf cars. In February 2023, Nissan announced its intent to produce electric vehicles with solid-state batteries by 2028.In November 2023, Nissan announced its intention to build electric versions of the Qashqai and Juke at its plant in Britain.

Nissan_Leaf

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

P577 - publication date: date or point in time when a work was first published or released

P571 - inception: time when an entity begins to exist; for date of official opening use P1619

P1619 - date of official opening: date or point in time a place, organization, or event officially opened

P729 - service entry: date or point in time on which a piece or class of equipment entered operational service

P619 - UTC date of spacecraft launch: date of spacecraft launch in UTC

Q109682732 - 2009 Indonesian People's Representative Council election: 2009 election in Indonesia

Q53385935 - 2009-2010 one-year-period: period of about one year starting in 2009 and ending in 2010

Nissan_Leaf

Q4639535 - 5,000,000: album by Dread Zeppelin

Q130113239 - 500000 (Price Discovery))) (A Study on Price Discovery in KOSDAQ50 Index Spot and Futures Markets): scholarly article published in 2005

Nissan_Leaf

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_Leaf

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

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

Nissan_Leaf

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

Nissan_Leaf

Q16021 - 1,000,000,000: natural number with the value 10⁹, named one billion or one milliard

Q862978 - 1,000,000,000,000: natural number with the value 10¹²

Q803760 - 10¹⁵: natural number with value 10¹⁵, one thousand trillion, named one quadrillion or one billiard depending on the scale; also one thousand, million, million

Nissan_Leaf

Q1488587 - 200000 Danielparrott: asteroid

Nissan_Leaf

Q45922130 - December 31, 2018: Monday in December 2018

Nissan_Leaf

Nissan_Leaf

Q69301975 - December 29, 1997: date in Gregorian calendar

Nissan_Leaf

Nissan_Leaf

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

Nissan_Leaf

Q24717623 - June 1, 2016: Wednesday in June 2016

Q131608109 - 2016-06-01 Dynamo-2 Kyiv vs PFC Sumy: association football match at Dynamo Training Center in Koncha-Zaspa during 2015–16 Ukrainian First League with score 4:1

Nissan_Leaf

Nissan_Leaf

Nissan_Leaf

Q123662786 - DAVIS: weather station in Oates Land, Australia from 1957

Q18252030 - Lef1: protein-coding gene in the species Mus musculus

Q18302624 - Pigk: protein-coding gene in the species Mus musculus

Q18265473 - Rbm12b1: protein-coding gene in the species Mus musculus

Nissan_Leaf

Nissan_Leaf

Q18046689 - MED28: protein-coding gene in the species Homo sapiens

Q18248075 - Calm2: protein-coding gene in the species Mus musculus

Q18258464 - Suclg1: protein-coding gene in the species Mus musculus

Q18261535 - Atp5d: protein-coding gene in the species Mus musculus

Nissan_Leaf

Q33122591 - March 1, 2017: Wednesday in March 2017

Q123668913 - 2017-03-01 Northwestern v. Michigan basketball game: None

Nissan_Leaf

Q15586 - Quaoar: dwarf planet in the Kuiper belt

Q7811749 - Tobu 50000 series: Electric multiple unit train type operated by Tobu Railway in Japan

Q863629 - Odakyu 50000 series VSE: electric multiple unit train operated by Odakyu Electric Railway in Japan

Nissan_Leaf

Q69308512 - January 1, 2028: date in Gregorian calendar

Nissan_Leaf

Nissan_Leaf

Q69306970 - November 1, 2023: date in Gregorian calendar

Q131379942 - 2023-11-01 Handball Thüringer HC - HSG Bensheim/Auerbach: None

Nissan_Leaf

Q4644323 - 80,000 Hours: organization researching how to have an impactful career

Q270979 - 80000 Shots: 2011 film by Manfred Walther

Nissan_Leaf

Q145 - United Kingdom: country in north-west Europe

Q174193 - United Kingdom of Great Britain and Ireland: historical sovereign state (1801–1922)

Q7979 - British English: forms of the English language used in Britain

Q161885 - Kingdom of Great Britain: constitutional monarchy in Western Europe (1707–1800)

Q11010 - Parliament of the United Kingdom: supreme legislative body of the United Kingdom

Nissan_Leaf

Q69306696 - February 1, 2023: date in Gregorian calendar

Q123667718 - 2023-02-01 TAU Castelló vs Gipuzkoa Basket: None

Q123688570 - 2023-02-01 Handball Thüringer HC - HSG Bensheim/Auerbach: None

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

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

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

Autonomous cars

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

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

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

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nissan, Autonomous cars: In August 2013 Nissan announced its plans to launch severaldriverless carsby 2020. The company built a dedicated autonomous driving proving ground in Japan, completed in 2014. Nissan installed its autonomous car technology in aNissan Leafall-electric carfor demonstration purposes and was demonstrated at Nissan 360 test drive event held in California in August 2013.In September 2013, the Leaf fitted with the prototype Advanced Driver Assistance System was granted a license plate that allows it to drive on Japanese public roads. The testing car was used by Nissan engineers to evaluate how its in-house autonomous driving software performs in the real world. Time spent on public roads will help refine the car's software for fully automated driving.The autonomous Leaf was demonstrated on public roads for the first time at a media event held in Japan in November 2013. The Leaf drove on the Sagami Expressway inKanagawa Prefecture, near Tokyo. Nissan vice chairmanToshiyuki Shigaand the prefecture's governor, Yuji Kuroiwa, rode in the car during the test.

[
    "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)"
]

[
    "Q741490 - autonomous car: automobile capable of traveling without human input",
    "Q65042307 - Autonomous cargo ship: Crewless vehicles that transport cargo",
    "Q28324955 - self-driving car liability: area of public liability law",
    "Q123996357 - Autonomous cart: US patent 11269335",
    "Q123594758 - Autonomous cargo loading systems and methods: US patent 11440195"
]

[
    "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"
]

[
    "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"
]

[
    "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)"
]

[
    "Q25337 - 2020: year",
    "Q5978933 - 2020: natural number",
    "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"
]

[
    "Q127513 - Kanagawa Prefecture: prefecture of Japan",
    "Q90558818 - COVID-19 pandemic in Kanagawa prefecture: None",
    "Q106837367 - Kanagawa Prefecture Station: Future station of Chuo Shinkansen maglev in Midori-ku, Sagamihara, Kanagawa prefecture",
    "Q11589226 - Kanagawa Prefecture Natural Environment Conservation Center: government organization in Atsugi, Japan",
    "Q10340050 - flag of Kanagawa prefecture: flag used by Kanagawa prefecture, Japan"
]

[
    "Q3532645 - Toshiyuki Shiga: President and Chief Operating Officer of Nissan"
]

[
    "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"
]

[
    "Q741490 - autonomous car: automobile capable of traveling without human input",
    "Q47410709 - Autonomous Cars: In Favor of a Mandatory Ethics Setting: scientific article published on 14 July 2016",
    "Q128482213 - Autonomous cars learn from terrible driving: scholarly article",
    "Q128591312 - Autonomous Cars: Social and Economic Implications: scholarly article",
    "Q129206054 - Autonomous Cars: Research Results, Issues, and Future Challenges: scholarly article"
]

[
    "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"
]

[
    "Q1999 - 2014: year",
    "Q5371769 - 2014: natural number",
    "Q9678 - 2014 Winter Olympics: 22nd edition of Winter Olympics, in Sochi, Russia",
    "Q108816797 - 2014 Indonesian People's Representative Council election: None",
    "Q16735873 - 2014 Macedonian general election: None"
]

[
    "Q17982020 - September 1, 2013: date",
    "Q131609374 - 2013-09-01 Dynamo Kyiv vs Dnipro: association football match at NSC Olimpiyskyi during 2013–14 Ukrainian Premier League with score 1:1"
]

[
    "Q99 - California: state of the United States of America",
    "Q16047894 - California: genus of plants",
    "Q93480414 - California: vocal track by Grimes; 2015 studio recording",
    "Q104057194 - California: Italian singer and disc jockey (1990-)",
    "Q161562 - California Institute of Technology: private research university located in Pasadena, California, USA"
]

[
    "Q655960 - Yūji Kuroiwa: Japanese politician and television director"
]

Output: None of the candidates

Output: None of the candidates


Nissan

Autonomous cars

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

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

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nissan, Autonomous cars: In August 2013 Nissan announced its plans to launch severaldriverless carsby 2020. The company built a dedicated autonomous driving proving ground in Japan, completed in 2014. Nissan installed its autonomous car technology in aNissan Leafall-electric carfor demonstration purposes and was demonstrated at Nissan 360 test drive event held in California in August 2013.In September 2013, the Leaf fitted with the prototype Advanced Driver Assistance System was granted a license plate that allows it to drive on Japanese public roads. The testing car was used by Nissan engineers to evaluate how its in-house autonomous driving software performs in the real world. Time spent on public roads will help refine the car's software for fully automated driving.The autonomous Leaf was demonstrated on public roads for the first time at a media event held in Japan in November 2013. The Leaf drove on the Sagami Expressway inKanagawa Prefecture, near Tokyo. Nissan vice chairmanToshiyuki Shigaand the prefecture's governor, Yuji Kuroiwa, rode in the car during the test.

Nissan

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)

Q741490 - autonomous car: automobile capable of traveling without human input

Q65042307 - Autonomous cargo ship: Crewless vehicles that transport cargo

Q28324955 - self-driving car liability: area of public liability law

Q123996357 - Autonomous cart: US patent 11269335

Q123594758 - Autonomous cargo loading systems and methods: US patent 11440195

Nissan

Nissan

Q19767869 - December 31, 2014: Wednesday in December 2014

Nissan

Nissan

Q131609374 - 2013-09-01 Dynamo Kyiv vs Dnipro: association football match at NSC Olimpiyskyi during 2013–14 Ukrainian Premier League with score 1:1

Nissan

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

Nissan

Q53097 - Acura: car brand and luxury vehicle division of Japanese automaker Honda Motor Company

Q2263998 - Acura RLX: mid-size luxury sedan manufactured by Honda and sold under their Acura division

Q1426408 - Honda Integra: series of automobiles produced by Honda

Nissan

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

Nissan

Q52437 - Murano: series of islands in the Venetian Lagoon, Italy

Q25340700 - Murano: village in Kaimana Regency, West Papua, Indonesia

Q27156984 - Murano: Japanese family name (村野)

Q63383737 - Murano Colonna "B": ACTV stop in Venice on Murano, Italy

Nissan

Q12718242 - SSR: database of place names in Norway from the Norwegian Mapping Authority

Q14720544 - Sentral Baru: village in Rejang Lebong Regency, Bengkulu, Indonesia

Q24701684 - Sentral Sari: village in Banggai Regency, Central Sulawesi, Indonesia

Q4700653 - Akershus University Hospital: hospital in Lørenskog, Akershus, Norway

Q801042 - Kuala Lumpur Sentral station: railway station

Nissan

Q43261 - horizon: apparent line that separates earth from sky

Q135075090 - Skyline: 2025 video game developed by Baknight

Q465837 - skyline: shape of a city's buildings against the sky

Q7537906 - Skyline High School: secondary school in Dallas, Texas, United States

Q1340087 - Skyline: 2010 film by Greg and Colin Strause

Nissan

Q7678557 - Takuma: undifferentiated Japanese kana male given name (たくま)

Q171297 - Takuma Sato: Japanese race car driver

Q528279 - Takuma Ueda: Japanese badminton player

Nissan

Q1135638 - Nissan X-Trail: automobile model series

Nissan

Q7850 - Chinese: language group of the Sinitic languages

Q258 - South Africa: country in southern Africa

Q9751 - Z/z: letter of the Latin alphabet

Q954 - Zimbabwe: sovereign state in southern Africa

Q953 - Zambia: country at the crossroads of Central and Southern Africa

Nissan

Nissan

Nissan

Q11452 - general relativity: standard and classical physics theory of gravity and space

Q60668873 - Gateway to Research: database of publicly funded research in the UK

Q18737011 - Govia Thameslink Railway: British train operating company

Q1423320 - GTR – FIA GT Racing Game: 2004 video game

Nissan

Q838268 - Patrol: district in Indramayu Regency, West Java Province, Indonesia

Q13466655 - Patrol: village in Indramayu Regency, West Java, Indonesia

Q11379765 - patrol: operational independent, professionally watching and patrolling of an assigned scope by security forces

Q331795 - patrol vessel: naval watercraft designed for monitoring sea and littoral areas

Nissan

Q215601 - pickup truck: light-duty truck with an enclosed cab and an open cargo area

Q7190940 - Pickup Truck Racing: motor vehicle

Q114518440 - Pickup Trucks: episode of Modern Marvels (S9 E43)

Q7190944 - pickup truck racing: auto racing with modified pickup trucks

Nissan

Q69872130 - van: lowercase family name prefix

Q80550 - Van Province: province of Turkey

Q69876093 - Van: uppercase Flemish family name prefix

Nissan

Q105428185 - Caravan: instrumental track by Duke Ellington and his Famous Orchestra; 1937 studio recording

Q105427467 - Caravan: instrumental track by Barney Bigard and his Jazzopaters; 1936 studio recording

Q11194338 - Caravan: Japanese singer-songwriter

Q186347 - caravanserai: type of roadside inn

Nissan

Q10801450 - Nissan Cabstar: pickup trucks and light commercial vehicle

Nissan

Q181014 - fugue: contrapuntal musical form based on a subject that recurs in imitation

Q113565890 - Fuga: Japanese human beatboxer

Nissan

Q1929383 - sneaker: footwear with a flexible rubber or synthetic sole and a fabric, cloth, or leather upper

Q116269566 - Kicks: vocal track by Paul Revere & the Raiders; 1966 studio recording

Q34755 - Kickstarter: US-based crowdfunding platform

Q1862763 - Kicks: 2007 film by Albert ter Heerdt

Q7618859 - Florida Strikers: American soccer team (1994–1997)

Nissan

Q33971 - leaf: main organ of photosynthesis and transpiration in higher plants, usually consisting of a flat green blade attached to the stem directly or by a stalk

Q107256474 - leaf: unit of extent that consists of a single bound or fastened sheet as a subunit of a volume; each leaf consists of two pages, one on each side, either or both of which may be blank

Q244452 - Cicadellidae: family of insects

Nissan

Q169925 - Mozilla Corporation: American software company

Q488659 - Montgomery County: county in Maryland, United States

Q11440555 - Museum of Oriental Ceramics, Osaka: art museum in Osaka, Japan

Q21108047 - Mo-molybdopterin cofactor biosynthetic process: The chemical reactions and pathways resulting in the formation of the Mo-molybdopterin cofactor, essential for the catalytic activity of some enzymes. The cofactor consists of a mononuclear molybdenum (Mo) ion coordinated by one or two molybdopterin

Nissan

Q4360 - pulsar: highly magnetized, rapidly rotating neutron star or white dwarf

Q135452961 - Pulsar: 2024 video game developed by Construct Games

Q134981306 - PULSAR: 2020 video game developed by Bruno Esteves

Q3320731 - Monica Rambeau: fictional character in Marvel Comics

Nissan

Q99744403 - Senzo Mazingiza: South African sports executive

Q48811309 - Senzo: album by Abdullah Ibrahim

Q101438665 - Senzo Radebe: South African actor

Q7451444 - Senzoku Gakuen College of Music: Higher education institution in Kanagawa Prefecture, Japan

Nissan

Q113178626 - Silvia Resconi: Italian particle physicist

Q152308 - Queen Silvia of Sweden: queen consort of Sweden

Nissan

Q88392887 - part of a work: part that a work consists of (use a more specific object if possible)

Q1756348 - stanza: grouped set of lines within a poem

Q207804 - strophe: first part of the ode; structural division of a poem containing stanzas of varying line length

Q104840874 - Stanza: Python-based natural language processing toolkit

Nissan

Q13630 - Olbia: Italian commune in Sardinia

Nissan

Q70752288 - Ultramax: American pornographic actress

Q122767652 - Ultramax: ship built in 1973

Q2638662 - Albert Roder: German engineer (1896-1970)

Nissan

Q1042721 - Paola Cortellesi: Italian actress and singer (born 1973)

Q85263471 - Vanette E J McLennan: researcher

Q16866493 - Vanette W. Johnson: American football coach

Q49002625 - Vanettes Dam: dam in Powder River County, Montana, United States of America

Nissan

Q780756 - Auster Aircraft Limited: 1938-1961 aircraft manufacturer

Q193724 - Slavkov u Brna: town in the Czech Republic

Q1681626 - Auster: 1942 liaison aircraft family by Taylorcraft

Nissan

Q809486 - Bartolomeo Bassante: Italian painter (1618-1648)

Q84786401 - Bassant M M Ibrahim: researcher

Q25435287 - Basant Hemida: Egyptian female sprinter

Nissan

Q208257 - Bedford: county town of Bedfordshire, England

Q894077 - Bedford: unitary authority area with the status of a borough in the ceremonial county of Bedfordshire, England, UK

Q1762823 - Bedford: city in Lawrence County, Indiana, United States

Nissan

Q21077467 - Goudsmit-Hoff: cycling team (1960-1972)

Q27895700 - Caballeronia: genus of Betaproteobacteria

Q58657853 - Daniel Caballero: researcher ORCID ID = 0000-0003-2822-0323

Nissan

Q27543 - Datsun: automobile brand of Nissan

Q117080876 - Datsun: general cargo vessel built in 2005

Q122948395 - Datsun: Norwegian musical group

Q1664019 - The Datsuns: hard rock band from Cambridge, New Zealand

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

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

Non-automotive products

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

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

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

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nissan, Non-automotive products: Nissan has also had a number of ventures outside the automotive industry, most notably the Tu–Ka mobile phone service (est. 1994), which was sold to DDI and Japan Telecom (both now merged intoKDDI) in 1999. Nissan offers a subscription-basedtelematicsservice in select vehicles to drivers in Japan, calledCarWings. Nissan also ownsNissan Marine, a joint venture withTohatsuCorp that produces motors for smaller boats and other maritime equipment. Nissan also built solid rocket motors for orbital launch vehicles such as theLambda 4SandM-V.The aerospace and defense division of Nissan was sold toIHI Corporationin 2000.

[
    "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)"
]

[
    "Q2091 - 1999: year",
    "Q842838 - 1999: natural number",
    "Q4881915 - 1999 Belarus Census: None",
    "Q208824 - 1999: 1982 studio album by Prince and the Revolution",
    "Q451110 - 1999 German presidential election: presidential election by 11th federal convention"
]

[
    "Q718753 - drug interaction: change in the action or side effects of a drug caused by another drug",
    "Q422606 - didanosine: chemical compound",
    "Q12242304 - Dasman Diabetes Institute: Education organization in Kuwait City, Kuwait",
    "Q86679777 - Decca Dischi Italia S.p.A.: Italian Record Company; Italian branco of Decca Records",
    "Q28600458 - DDI: sector-based disk image format"
]

[
    "Q108279130 - Japan Telecom: periodical",
    "Q7553832 - SoftBank Telecom: Japanese telephone company",
    "Q96325954 - Japan Telecommunications Review: None"
]

[
    "Q971946 - KDDI: Japanese telecommunications operator",
    "Q11227691 - KDDI Research: company in Saitama, Japan",
    "Q11227689 - KDDI Building: None",
    "Q52338885 - KDDI Pacific Link: cable layer built in 1993",
    "Q76313620 - KDDI 2006 data breach: None"
]

[
    "Q7041006 - Nissan Marine: marine engine manufacturer"
]

[
    "Q5037198 - CarWings: vehicle telematics service"
]

[
    "Q1653679 - IHI Corporation: manufacturing company in Japan",
    "Q29122978 - IHI Corporation (United States): None",
    "Q2159756 - IHI Corporation F7: 2000s Japanese turbofan aircraft engine",
    "Q11254783 - IHI Corporation XF5: 2010s Japanese turbofan aircraft engine",
    "Q60807097 - IHI Corporation XF9: 2010s Japanese turbofan aircraft engine"
]

[
    "Q1985 - 2000: calendar year",
    "Q241253 - 2000: natural number",
    "Q8544 - 2000 Summer Olympics: Games of the XXVII Olympiad, in Sydney, Australia",
    "Q35024 - 2000s: decade from 2000 to 2009",
    "Q855688 - 2000 Summer Paralympics: multi-parasport event in Sydney, Australia"
]

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


Nissan

Non-automotive products

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

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

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nissan, Non-automotive products: Nissan has also had a number of ventures outside the automotive industry, most notably the Tu–Ka mobile phone service (est. 1994), which was sold to DDI and Japan Telecom (both now merged intoKDDI) in 1999. Nissan offers a subscription-basedtelematicsservice in select vehicles to drivers in Japan, calledCarWings. Nissan also ownsNissan Marine, a joint venture withTohatsuCorp that produces motors for smaller boats and other maritime equipment. Nissan also built solid rocket motors for orbital launch vehicles such as theLambda 4SandM-V.The aerospace and defense division of Nissan was sold toIHI Corporationin 2000.

Nissan

Tu–Ka

Q9663 - 1994 Winter Olympics: 17th edition of Winter Olympics, in Lillehammer, Norway

Q101751 - 1994 FIFA World Cup: 15th FIFA World Cup, held in United States

Q702017 - 1994 German federal election: election

Tu–Ka

P7888 - merged into: the subject was dissolved and merged into the object, that existed previously

P11812 - afterward owned by: person or institution that owned the subject after stated event. Meant to be used as qualifier of “significant event (P793)” for events like sales, auctions, gifts, etc

P11811 - beforehand owned by: person or institution that owned the subject just before stated event. Meant to be used as qualifier of "significant event (P793)" for events like sales, auctions, gifts, etc

Q718753 - drug interaction: change in the action or side effects of a drug caused by another drug

Q12242304 - Dasman Diabetes Institute: Education organization in Kuwait City, Kuwait

Q86679777 - Decca Dischi Italia S.p.A.: Italian Record Company; Italian branco of Decca Records

Q28600458 - DDI: sector-based disk image format

Tu–Ka

Q7553832 - SoftBank Telecom: Japanese telephone company

Q96325954 - Japan Telecommunications Review: None

Nissan

Q5037198 - CarWings: vehicle telematics service

Nissan

Q7041006 - Nissan Marine: marine engine manufacturer

Nissan_Marine

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).

Nissan

Q5241097 - Lambda 4S: expendable carrier rocket

Nissan

Nissan

Q1653679 - IHI Corporation: manufacturing company in Japan

Q29122978 - IHI Corporation (United States): None

Q2159756 - IHI Corporation F7: 2000s Japanese turbofan aircraft engine

Q11254783 - IHI Corporation XF5: 2010s Japanese turbofan aircraft engine

Q60807097 - IHI Corporation XF9: 2010s Japanese turbofan aircraft engine

Nissan

Q8544 - 2000 Summer Olympics: Games of the XXVII Olympiad, in Sydney, Australia

Q855688 - 2000 Summer Paralympics: multi-parasport event in Sydney, Australia

Output: None of the candidates

Output: None of the candidates

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Output: None of the candidates