Brands

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: Mars_Inc, Brands: Many Mars products are household, famous-name brands. Some of these product lines are manufactured by Mars; others are manufactured by The Wrigley Company.

Mars,_Inc.

Mars,_Inc.

Q1052807 - Wrigley Field: baseball stadium in Chicago, Illinois, USA; home venue of the Chicago Cubs

Q819241 - Wrigley Company: American company headquartered in Chicago, Illinois

Q5858659 - Fenella Ann Wrigley: botanist

Q375710 - Wrigley: human settlement in the Northwest Territories, Canada

Mars,_Inc.

Mars,_Inc.

Q1056637 - The Hershey Company: American corporation

Q6861148 - Milton Hershey School: private boarding school in Hershey, Pennsylvania, United States

Q2893738 - Hershey's Kisses: Chocolate candy

Q5744519 - Hershey Creamery Company: American pre-packaged ice cream and cold dessert maker

Mars,_Inc.

Q714179 - Snickers: brand name chocolate bar made by Mars, Incorporated

Q886003 - Bluemont: unincorporated village in Loudoun County, Virginia, United States

Q1748734 - Snickers Workwear: clothing brand

Q4405719 - Snickers salad: US dessert dish of Snickers bars, apples, and whipped cream

Mars,_Inc.

Q869733 - Twix: chocolate-coated cookie candy bar

Q2408534 - Twixt: 2011 film directed by Francis Ford Coppola

Q1362781 - TwixT: One of the two-player strategy board games in the 3M bookshelf game series

Mars,_Inc.

Q739550 - M&M's: brand of chocolate candy pieces

Q6711826 - M&M's Adventure: 2008 video game

Q16737028 - M&M's Kart Racing: 2007 video game

Q3272256 - M&M's World: store that sells "M&M's"

Q252623 - Pennsylvania 400: NASCAR Cup Series stock car race held in Long Pond, Pennsylvania, USA

Mars,_Inc.

Q8921300 - skittles: old European lawn game

Q868612 - Skittles: brand of sugar-coated fruit flavored candy

Q29810085 - Skittles Dmel_CG9985: protein found in Drosophila melanogaster

Q29711548 - sktl: protein-coding gene in the species Drosophila melanogaster

Q106797987 - Skittles: painting by Jan Both

Mars,_Inc.

Q726611 - starburst galaxy: galaxy undergoing an exceptionally high rate of star formation

Q1186164 - spin-off company: type of organization which is the outcome of a corporate spin-off

Q7601502 - Starburst: British science fiction magazine and webzine

Mars,_Inc.

Mars,_Inc.

Mars,_Inc.

Mars,_Inc.

Mars,_Inc.

Mars,_Inc.

Q8043281 - Xavier Musketeers: athletic program of Xavier University in Cincinnati

Q8043283 - Xavier Musketeers men's basketball: men's basketball team that represents Xavier University

Q7061192 - The Musketeers: 1961 film by Annelise Hovmand

Q1564383 - Musketeers of the Guard: elite fighting company in France

Q4309114 - Musketeers Twenty Years After: 1992 film by George Jungvald-Khilkevitch

Mars,_Inc.

Mars,_Inc.

Mars,_Inc.

Mars,_Inc.

Q6648982 - pet food: food for domestic animals

Q106116788 - Petfood industry: magazine of pet food manufacturing and merchandising

Q28368538 - Petfood applications of inulin and oligofructose: scientific article

Mars,_Inc.

Q7100035 - Orbit: medical journal focusing on orbital disorders

Q4130 - orbit: curved path of an object around a point

Q3355196 - Orbit: international group of sister imprints that specialise in science fiction and fantasy books

Q156075 - orbit: cavity or socket of the skull in which the eye and its appendages are situated

Q122823380 - Orbit: 2022 early access video game

Mars,_Inc.

Q122821793 - Bounty: 2022 early access video game

Q1211716 - The Bounty: 1984 film directed by Roger Donaldson

Q895167 - Bounty: chocolate bar manufactured by Mars, Incorporated

Mars,_Inc.

Q133948 - antioxidant: molecule that inhibits the oxidation of other molecules

Q27726919 - Antioxidants (Basel, Switzerland): scientific journal

Q4775078 - Antioxidants & Redox Signaling: peer-reviewed scientific journal

Q64354733 - Antioxidants and Pulmonary Rehabilitation in COPD Patients: clinical trial

Q57257552 - Antioxidants: article from 2000

Mars,_Inc.

Mars,_Inc.

Q16412771 - Pedigree: Estonian musical group

Q189977 - family tree: chart representing family relationships in a conventional tree structure

Q38829 - breed: group of domestic animals with a distinctive phenotype

Q3242583 - pedigree chart: diagram showing the occurrence and appearance or phenotypes of a particular gene or organism, in generations

Q2027177 - Pedigree Petfoods: UK dog food manufacturer

Mars,_Inc.

Mars,_Inc.

Q332062 - Sorghum bicolor: species of plant

Mars,_Inc.

Q2245650 - Kudos: British independent film and television production company

Q2712090 - Sinclair QDOS: multitasking operating system

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


Original food 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: Mars_Inc, Original food products: Note: manufactured byWrigley Company

[
    "Q695087 - Mars, Incorporated: American global food company and manufacturer",
    "Q134584046 - Mars, Inc.: 2013 novel by Ben Bova"
]

[
    "Q819241 - Wrigley Company: American company headquartered in Chicago, Illinois"
]

[
    "Q2095 - food: any substance consumed to provide nutritional support for the body; form of energy stored in chemical",
    "Q8195619 - human food: food eaten by Homo sapiens",
    "Q173710 - esophagus: vertebrate organ through which food passes to the stomach",
    "Q3007875 - Food Chemistry: peer-reviewed scientific journal",
    "Q130365 - riboflavin: chemical compound"
]

[
    "Q695087 - Mars, Incorporated: American global food company and manufacturer",
    "Q134584046 - Mars, Inc.: 2013 novel by Ben Bova"
]


Original food 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: Mars_Inc, Original food products: Note: manufactured byWrigley Company

Mars,_Inc.

Q695087 - Mars, Incorporated: American global food company and manufacturer

Q134584046 - Mars, Inc.: 2013 novel by Ben Bova

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

Q819241 - Wrigley Company: American company headquartered in Chicago, Illinois

Mars,_Inc.

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

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

Q2095 - food: any substance consumed to provide nutritional support for the body; form of energy stored in chemical

Q8195619 - human food: food eaten by Homo sapiens

Q173710 - esophagus: vertebrate organ through which food passes to the stomach

Q3007875 - Food Chemistry: peer-reviewed scientific journal

Mars,_Inc.

Output: None of the candidates


Veterinary services

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: Mars_Inc, Veterinary services: Mars Veterinary Health North America Mars Veterinary Health International

[
    "Q695087 - Mars, Incorporated: American global food company and manufacturer",
    "Q123255524 - Mars Inc. awards $4 million for health care in Ukraine: scientific article published in 2023"
]


Veterinary services

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: Mars_Inc, Veterinary services: Mars Veterinary Health North America Mars Veterinary Health International

Mars_Inc

Q123255524 - Mars Inc. awards $4 million for health care in Ukraine: scientific article published in 2023

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).

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

Mars_Veterinary_Health

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

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

Q941023 - Eastern Time Zone: time zone observing UTC−05:00 during standard time and UTC−04:00 during daylight saving time

Q2086913 - Central Time Zone: time zone observing UTC−06:00 during standard time and UTC−05:00 during daylight saving time

Q19464773 - Northern America and Mexico: Canada, USA, Mexico, and Greenland

Q627368 - blastomycosis: primary systemic mycosis that results in a systemic fungal infection, and has material basis in Blastomyces dermatitidis

Mars_Veterinary_Health

Q423048 - International Standard Name Identifier: authority file with 16-digit identifier for people and organisations

Q33057 - International Standard Book Number: unique numeric book identifier

Q1072012 - internationality: something involving more than a single country

Q37156 - IBM: American multinational technology corporation

Q40970 - International Olympic Committee: non-governmental sports organisation (founded 1894)

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


Awards and honors

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: Mars_Inc, Awards and honors: The company was named byFortunemagazine as one of the top 100 companies to work for in 2013, citing the example that employees of the pet food division can take their dogs to work. The company has made donations toElizabethtown College, which includes a room sponsored by them and a weekly executive lecture series.

[
    "Q695087 - Mars, Incorporated: American global food company and manufacturer",
    "Q134584046 - Mars, Inc.: 2013 novel by Ben Bova"
]

[
    "Q1998 - 2013: year",
    "Q1489644 - 2013: natural number",
    "Q53386030 - 2013-2014 one-year-period: period of about one year starting in 2013 and ending in 2014",
    "Q114717229 - World Economic Forum Annual Meeting 2013: None",
    "Q1280387 - 2013 UCI Europe Tour: None"
]

[
    "Q695087 - Mars, Incorporated: American global food company and manufacturer",
    "Q134584046 - Mars, Inc.: 2013 novel by Ben Bova"
]

[
    "Q6648982 - pet food: food for domestic animals",
    "Q7171541 - Pet Food Express: American pet food store chain",
    "Q130861424 - Pet food compositions: US patent 11260099",
    "Q130860966 - Pet food compositions: US patent 11331336",
    "Q124434822 - Pet food compositions including probiotics and methods of manufacture and use thereof: US patent 11510424"
]

[
    "Q5363873 - Elizabethtown College: private college in Elizabethtown, Pennsylvania, U.S.",
    "Q104877952 - Elizabethtown Blue Jays: intercollegiate sports teams of Elizabethtown College"
]


Awards and honors

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: Mars_Inc, Awards and honors: The company was named byFortunemagazine as one of the top 100 companies to work for in 2013, citing the example that employees of the pet food division can take their dogs to work. The company has made donations toElizabethtown College, which includes a room sponsored by them and a weekly executive lecture series.

Mars,_Inc.

Q6648982 - pet food: food for domestic animals

Q7171541 - Pet Food Express: American pet food store chain

Q130861424 - Pet food compositions: US patent 11260099

Q130860966 - Pet food compositions: US patent 11331336

Q124434822 - Pet food compositions including probiotics and methods of manufacture and use thereof: US patent 11510424

Mars,_Inc.

Q5363873 - Elizabethtown College: private college in Elizabethtown, Pennsylvania, U.S.

Q104877952 - Elizabethtown Blue Jays: intercollegiate sports teams of Elizabethtown College

Mars,_Inc.

P166 - award received: award or recognition received by a person, organization or creative work

P1346 - winner: winner of a competition or similar event, not to be used for awards (instead use "award received" (P166) on awardee's item, possibly qualified with "for work" (P1686)) or for wars or battles

P1027 - conferred by: person or organization who grants an award, certification, grant, or role

P1686 - for work: qualifier of award received (P166) to specify the work that an award was given to the creator for

P1706 - together with: qualifier to specify the item that this property is shared with

Mars,_Inc.

Mars,_Inc.

Mars,_Inc.

Mars,_Inc.

Q53386030 - 2013-2014 one-year-period: period of about one year starting in 2013 and ending in 2014

Q114717229 - World Economic Forum Annual Meeting 2013: 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


Consumer relations

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: Mars_Inc, Consumer relations: Mars remains a family business owned by theMars family. The company is famous for its secrecy. A 1993Washington Post Magazinearticle was a rare raising of the veil, as the reporter was able to see the "M"s being applied to the M&M's, something that "no outsider had ever before been invited to observe".In 1999, for example, the company did not acknowledge that Forrest Mars Sr. had died or that he had worked for the company.As of 2019, the company had "aggressively" moved away from its model of secrecy through the media and sponsorships.

[
    "Q695087 - Mars, Incorporated: American global food company and manufacturer",
    "Q134584046 - Mars, Inc.: 2013 novel by Ben Bova"
]

[
    "Q3065338 - Mars family: American family",
    "Q117604381 - Mars Family Cemetery: cemetery in McCormick County, South Carolina, United States"
]

[
    "Q739550 - M&M's: brand of chocolate candy pieces",
    "Q6711826 - M&M's Adventure: 2008 video game",
    "Q16737028 - M&M's Kart Racing: 2007 video game",
    "Q3272256 - M&M's World: store that sells \"M&M's\"",
    "Q252623 - Pennsylvania 400: NASCAR Cup Series stock car race held in Long Pond, Pennsylvania, USA"
]

[
    "Q2065 - 1993: year",
    "Q858412 - 1993: natural number",
    "Q53385494 - 1993-1994 one-year-period: period of about one year starting in 1993 and ending in 1994",
    "Q1633212 - 1993 Spanish general election: election",
    "Q700458 - 1993–94 Fußball-Bundesliga: 31st season of the Bundesliga"
]

[
    "Q2578399 - Forrest Mars: American businessman (1904–1999)"
]

[
    "Q25274 - 2019: year",
    "Q12681717 - 2019: natural number",
    "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"
]

[
    "Q695087 - Mars, Incorporated: American global food company and manufacturer",
    "Q134584046 - Mars, Inc.: 2013 novel by Ben Bova"
]


Consumer relations

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: Mars_Inc, Consumer relations: Mars remains a family business owned by theMars family. The company is famous for its secrecy. A 1993Washington Post Magazinearticle was a rare raising of the veil, as the reporter was able to see the "M"s being applied to the M&M's, something that "no outsider had ever before been invited to observe".In 1999, for example, the company did not acknowledge that Forrest Mars Sr. had died or that he had worked for the company.As of 2019, the company had "aggressively" moved away from its model of secrecy through the media and sponsorships.

Mars,_Inc.

Q739550 - M&M's: brand of chocolate candy pieces

Q6711826 - M&M's Adventure: 2008 video game

Q16737028 - M&M's Kart Racing: 2007 video game

Q3272256 - M&M's World: store that sells "M&M's"

Q252623 - Pennsylvania 400: NASCAR Cup Series stock car race held in Long Pond, Pennsylvania, USA

Mars,_Inc.

Q557614 - McVitie's: British snack food brand

Mars,_Inc.

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

Mars,_Inc.

Q714179 - Snickers: brand name chocolate bar made by Mars, Incorporated

Q886003 - Bluemont: unincorporated village in Loudoun County, Virginia, United States

Q1748734 - Snickers Workwear: clothing brand

Q4405719 - Snickers salad: US dessert dish of Snickers bars, apples, and whipped cream

Mars,_Inc.

Q8921300 - skittles: old European lawn game

Q868612 - Skittles: brand of sugar-coated fruit flavored candy

Q29810085 - Skittles Dmel_CG9985: protein found in Drosophila melanogaster

Q29711548 - sktl: protein-coding gene in the species Drosophila melanogaster

Q106797987 - Skittles: painting by Jan Both

Mars,_Inc.

Q869733 - Twix: chocolate-coated cookie candy bar

Q2408534 - Twixt: 2011 film directed by Francis Ford Coppola

Q1362781 - TwixT: One of the two-player strategy board games in the 3M bookshelf game series

Mars,_Inc.

Q726611 - starburst galaxy: galaxy undergoing an exceptionally high rate of star formation

Q1186164 - spin-off company: type of organization which is the outcome of a corporate spin-off

Q7601502 - Starburst: British science fiction magazine and webzine

Mars,_Inc.

Q114504046 - Lifesavers: episode of Perfect Strangers (S2 E4)

Q114552392 - Lifesavers: episode of Diff'rent Strokes (S4 E22)

Q6545450 - Lifesavers Underground: Christian rock band

Q1824522 - Mixed Nuts: 1994 film by Nora Ephron

Q108902959 - Lifesavers Park: neighbourhood park in Hamilton, ON

Mars,_Inc.

Q115435123 - dairy-milk beverage: beverage containing dairy-milk

Q50529905 - Dairy milk fat augments paclitaxel therapy to suppress tumour metastasis in mice, and protects against the side-effects of chemotherapy: scientific article published on 8 July 2011

Q57814411 - Dairy milk, regardless of fat content, protects against postprandial hyperglycemia-mediated impairments in vascular endothelial function in adults with prediabetes by limiting oxidative stress responses that reduce nitric oxide bioavailability: article

Q58551599 - Dairy milk proteins attenuate hyperglycemia-induced impairments in vascular endothelial function in adults with prediabetes by limiting increases in glycemia and oxidative stress that reduce nitric oxide bioavailability: scientific article published on 25 September 2018

Q94490507 - Dairy Milk Is Associated with Increased Risk of Breast Cancer in the Adventist Health Study-2 (AHS-2) Cohort (P05-026-19): None

Mars,_Inc.

Mars,_Inc.

Q126734283 - Candy canes: observation tower in Hamburg

Mars,_Inc.

Q184357 - almond: edible seed of the almond tree

Q15545507 - Prunus amygdalus: species of plant

Q39918 - Prunus dulcis: species of plant

Mars,_Inc.

Q4982185 - jorgito: latin American brand of bubble gum

Mars,_Inc.

Q100724788 - The Banana Splits: American cartoon band from the tv series "The Banana Splits"

Q4853850 - Banana Splits discography: band discography

Q7715502 - The Banana Splits in Hocus Pocus Park: 1972 film by William Hanna and Joseph Barbera

Q1111916 - The Banana Splits: American television variety show produced by Hanna-Barbera Productions

Mars,_Inc.

Q98506918 - Milk Shake: 1968 nuclear weapons test

Q17511909 - Milk Shakes: mountain in Washington, United States of America

Q49050143 - Milk Shakes: mountain in Oregon, United States of America

Q44044785 - Milk shake-up: scientific article published on August 12, 1992

Mars,_Inc.

Q739929 - cafè mocha: chocolate-flavored variant of a caffè latte

Q4305160 - Mocha: river in Moskow Oblast, Russia, flows into the Pakhra

Mars,_Inc.

Q147651 - peanut butter: spread made primarily from ground dry roasted peanuts

Q56411770 - Peanut Butter: 1983 single by Gwen Guthrie

Q56411814 - Peanut Butter: 1985 single by Gwen Guthrie

Q7157922 - Peanut Butter Wolf's Jukebox 45's: 2002 compilation album; various artists

Q764044 - Clerodendrum trichotomum: species of plant

Mars,_Inc.

Q82168336 - Salty snacks and obesity--an unexpected link?: scientific article published on 01 April 2008

Mars,_Inc.

Q68200851 - Crispy: Danish bubblegum dance band

Q140096 - Crispijn van de Passe the Elder: Dutch engraver (1564-1637)

Q452530 - Chrispijn van den Broeck: Flemish painter, draughtsman and print designer (1523-1591)

Mars,_Inc.

Q940304 - Fudge: tabletop role-playing game system

Q5507059 - Fudge: American television series

Q503129 - chocolate brownie: baked chocolate confection

Mars,_Inc.

Q98755017 - Ice Cream: 2020 single by Blackpink and Selena Gomez

Q13233 - ice cream: frozen dessert made from frozen milk or cream, sugar, and a flavour

Q135017470 - Ice Cream: video game developed by Loolust

Q108575716 - Ice Cream Surfer: 2014 video game

Q5985233 - Ice Cream: 2012 single by HyunA

Mars,_Inc.

Q2937855 - Caramel: original song co-written and performed by Booba

Q183440 - caramel: confectionery product made by heating sugars

Q124425032 - Caramel: vocal track by Booba; 2012 studio recording

Q544992 - Caramel: 2007 film by Nadine Labaki

Q1178602 - Ferenc Molnár: Hungarian singer

Mars,_Inc.

Q58389 - House of Bourbon: European royal house of French origin

Q17070 - Réunion: overseas department of France in the Indian Ocean

Q543127 - bourbon whiskey: type of American whiskey, a barrel-aged distilled spirit made primarily from corn

Q627061 - Bourbonne-les-Bains: commune in Haute-Marne, France

Q245080 - Bourbon-l'Archambault: commune in Allier, France

Mars,_Inc.

Mars,_Inc.

Q38453 - Columbia: capital of South Carolina, United States

Mars,_Inc.

Q328313 - Houston Dynamo FC: soccer club in Houston, Texas, USA

Q1419711 - Orange Crush: single by R.E.M. from their 1988 album "Green"

Q7099604 - Orange Crush: novel by Tim Dorsey

Q77475276 - Orange Crush: painting by Larry Poons

Q9943 - Crush: line of fruit flavored carbonated beverages

Mars,_Inc.

Q27640062 - mint leaf: leaf of Mentha plant

Q98506933 - Mint Leaf: 1970 nuclear weapons test

Mars,_Inc.

Mars,_Inc.

Q113962013 - Juice: American rapper, songwriter, from Illinois

Q52151598 - Juice WRLD: American rapper and singer (1998–2019)

Mars,_Inc.

Q23905116 - Lemonade: 2016 studio album by Beyoncé

Q99522320 - Lemonade: 2020 single by Internet Money

Q893 - lemonade: lemon-flavored beverage

Q2757158 - Lemonade: song by Alexandra Stan

Mars,_Inc.

Q283 - water: chemical compound whose molecules are formed by two hydrogen atoms and one oxygen atom

Q7892 - drinking water: water safe for consumption

Q23392 - ice: water frozen into the solid state

Mars,_Inc.

Q146604 - Ribes nigrum: species of plant

Q2941309 - blackcurrant: fruit of the plant Ribes nigrum

Q115626563 - B Culture: magazine published by Just Above Midtown

Q21163398 - Black Currants: painting by Adriaen Coorte

Mars,_Inc.

Q524233 - Muskogee: city in Muskogee County, Oklahoma, United States

Q323461 - musk: class of aromatic substances used of perfumes, originally from scent glands of animals

Q21983870 - Muscle, skeletal, receptor tyrosine kinase: mammalian protein found in Mus musculus

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


Unethical treatment of animals

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: Mars_Inc, Unethical treatment of animals: In 2007, Mars came under criticism fromPeople for the Ethical Treatment of Animals(PETA) for funding laboratory experiments on mice, rats, guinea pigs and rabbits, which the group alleges are inhumane and in violation of the company's own policies prohibiting experiments on animals. One study was conducted in collaboration with theSalk Instituteregardingangiogenesisandspatial memory, in which mice were given anad-libdiet that includedepicatechin, plant-derived flavonoid. One of the experiments involved groups of control and experimental animals, the latter of which were housed individually in cages that included a running wheel for optional exercise for two hours a day. The former, also housed individually, did not have access to a running wheel. Another experiment was the classical spatial memory assay, theMorris water maze, in which experimenters had mice swim in water mixed with white paint that concealed the water depth. The study, which Mars contends was legally required in order for the company to make flavonoid-related health claims, showed that the inclusion of epicatechin in the diet improved memory and angiogenesis, more so if coupled with exercise.

[
    "Q695087 - Mars, Incorporated: American global food company and manufacturer",
    "Q134584046 - Mars, Inc.: 2013 novel by Ben Bova"
]

[
    "Q151888 - People for the Ethical Treatment of Animals: American animal rights organization",
    "Q3374987 - People for the Ethical Treatment of Animals v. Doughney: cybersquating",
    "Q111469883 - People For The Ethical Treatment Of Animals India: nonprofit organization from India",
    "Q110723805 - PETA UK: nonprofit organization"
]

[
    "Q539568 - angiogenesis: blood vessel formation when new vessels emerge from existing vessels",
    "Q15753544 - Angiogenesis: journal",
    "Q574834 - angiogenesis inhibitor: agent and endogenous substance that antagonizes or inhibits the development of new blood vessels",
    "Q29892230 - Angiogenesis: This pathway is a concise and simplified version of the basic proteins involved in angiogenesis. The proces is regulated by hyopoxia, which results in the transcription of eg. VEGF1, FGF2, PDGFbeta, MMP9, MMP2 and Ang1. These proteins bind to the...",
    "Q50430307 - angiogenesis modulating agents: agents that modulate the physiologic angiogenesis process"
]

[
    "Q2024 - 2007: calendar year",
    "Q907344 - 2007: natural number",
    "Q135047694 - 2007: video game developed by Hit'n'Run Digital Studio",
    "Q53389304 - 2007-2008 one-year-period: period of about one year starting in 2007 and ending in 2008",
    "Q15729072 - 2007 Census of the Philippines: item representing census of Philippines 2007 for source"
]

[
    "Q1417562 - Morris water navigation task: behavioral procedure used in behavioral neuroscience",
    "Q28296351 - Morris water maze: procedures for assessing spatial and related forms of learning and memory: scientific article",
    "Q48616085 - Morris Water Maze Deficits in Rats following Traumatic Brain Injury: Lateral Controlled Cortical Impact: scientific article published on September 1, 1997",
    "Q42153622 - Morris water maze test for learning and memory deficits in Alzheimer's disease model mice: scientific article",
    "Q35878754 - Morris water maze search strategy analysis in PDAPP mice before and after experimental traumatic brain injury: scientific article published on 23 November 2005"
]

[
    "Q23050136 - (−)-epicatechin: chemical compound",
    "Q5382492 - Epicatechin gallate: chemical compound",
    "Q7247556 - procyanidin C1: chemical compound",
    "Q7247551 - procyanidin B1: chemical compound",
    "Q7247550 - proanthocyanidin A2: chemical compound"
]

[
    "Q695087 - Mars, Incorporated: American global food company and manufacturer",
    "Q134584046 - Mars, Inc.: 2013 novel by Ben Bova"
]

[
    "Q16423649 - Peta: female given name",
    "Q37466502 - Peta: family name",
    "Q12505311 - Peta: subdistrict in Palopo City, South Sulawesi, Indonesia",
    "Q12798587 - Petar: male given name",
    "Q113443928 - Petar Kevin Rados: particle physicist"
]

[
    "Q1351061 - Salk Institute for Biological Studies: life sciences research institute",
    "Q128635401 - Salk Institute for Biological Studies building: building in La Jolla, California, United States",
    "Q46829000 - Salk Institute picks a new president: scientific article",
    "Q46138406 - Salk Institute president to step down: scientific article",
    "Q46331003 - Salk Institute president is replaced: scientific article published in December 1994"
]

[
    "Q539568 - angiogenesis: blood vessel formation when new vessels emerge from existing vessels",
    "Q15753544 - Angiogenesis: journal",
    "Q574834 - angiogenesis inhibitor: agent and endogenous substance that antagonizes or inhibits the development of new blood vessels",
    "Q29892230 - Angiogenesis: This pathway is a concise and simplified version of the basic proteins involved in angiogenesis. The proces is regulated by hyopoxia, which results in the transcription of eg. VEGF1, FGF2, PDGFbeta, MMP9, MMP2 and Ang1. These proteins bind to the...",
    "Q50430307 - angiogenesis modulating agents: agents that modulate the physiologic angiogenesis process"
]

[
    "Q23050136 - (−)-epicatechin: chemical compound",
    "Q5382492 - Epicatechin gallate: chemical compound",
    "Q7247556 - procyanidin C1: chemical compound",
    "Q7247551 - procyanidin B1: chemical compound",
    "Q7247550 - proanthocyanidin A2: chemical compound"
]

[
    "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."
]

Output: None of the candidates


Unethical treatment of animals

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: Mars_Inc, Unethical treatment of animals: In 2007, Mars came under criticism fromPeople for the Ethical Treatment of Animals(PETA) for funding laboratory experiments on mice, rats, guinea pigs and rabbits, which the group alleges are inhumane and in violation of the company's own policies prohibiting experiments on animals. One study was conducted in collaboration with theSalk Instituteregardingangiogenesisandspatial memory, in which mice were given anad-libdiet that includedepicatechin, plant-derived flavonoid. One of the experiments involved groups of control and experimental animals, the latter of which were housed individually in cages that included a running wheel for optional exercise for two hours a day. The former, also housed individually, did not have access to a running wheel. Another experiment was the classical spatial memory assay, theMorris water maze, in which experimenters had mice swim in water mixed with white paint that concealed the water depth. The study, which Mars contends was legally required in order for the company to make flavonoid-related health claims, showed that the inclusion of epicatechin in the diet improved memory and angiogenesis, more so if coupled with exercise.

Mars,_Inc.

Q539568 - angiogenesis: blood vessel formation when new vessels emerge from existing vessels

Q574834 - angiogenesis inhibitor: agent and endogenous substance that antagonizes or inhibits the development of new blood vessels

Q29892230 - Angiogenesis: This pathway is a concise and simplified version of the basic proteins involved in angiogenesis. The proces is regulated by hyopoxia, which results in the transcription of eg. VEGF1, FGF2, PDGFbeta, MMP9, MMP2 and Ang1. These proteins bind to the...

Q50430307 - angiogenesis modulating agents: agents that modulate the physiologic angiogenesis process

Mars,_Inc.

Q23050136 - (−)-epicatechin: chemical compound

Q5382492 - Epicatechin gallate: chemical compound

Q7247556 - procyanidin C1: chemical compound

Q7247551 - procyanidin B1: chemical compound

Q7247550 - proanthocyanidin A2: chemical compound

Mars,_Inc.

Q1417562 - Morris water navigation task: behavioral procedure used in behavioral neuroscience

Q28296351 - Morris water maze: procedures for assessing spatial and related forms of learning and memory: scientific article

Q48616085 - Morris Water Maze Deficits in Rats following Traumatic Brain Injury: Lateral Controlled Cortical Impact: scientific article published on September 1, 1997

Q42153622 - Morris water maze test for learning and memory deficits in Alzheimer's disease model mice: scientific article

Q35878754 - Morris water maze search strategy analysis in PDAPP mice before and after experimental traumatic brain injury: scientific article published on 23 November 2005

Mars,_Inc.

Q3560550 - spatial memory: memory about one's environment and spatial orientation

Q37569665 - Spatial memory, recognition memory, and the hippocampus: scholarly article

Q44136977 - Spatial memory and hippocampal function: scientific article published on January 1, 1979

Q33717296 - Spatial memory performances of aged rats in the water maze predict levels of hippocampal neurogenesis: scientific article published on November 12, 2003

Q31960817 - Spatial memory deficits in patients with lesions to the right hippocampus and to the right parahippocampal cortex: scientific article

Mars,_Inc.

Q3561192 - flavonoid: chemical compound containing two or more aromatic rings, each bearing at least one aromatic hydroxyl and connected with a carbon bridge

Q3234924 - isoflavonoid: any chemical compound having a structure based on 3-phenylchromane backbone

Q222174 - 2-phenylchromane flavonoid: any organic compound having a 2-phenylchromane skeleton

Q21123538 - flavonoid biosynthetic process: The chemical reactions and pathways resulting in the formation of flavonoids, a group of phenolic derivatives containing a flavan skeleton.

Q14909347 - flavonoid metabolic process: The chemical reactions and pathways involving flavonoids, a group of water-soluble phenolic derivatives containing a flavan skeleton including flavones, flavonols and flavanoids, and anthocyanins.

Mars,_Inc.

Q12505311 - Peta: subdistrict in Palopo City, South Sulawesi, Indonesia

Q113443928 - Petar Kevin Rados: particle physicist

Mars,_Inc.

Q1351061 - Salk Institute for Biological Studies: life sciences research institute

Q128635401 - Salk Institute for Biological Studies building: building in La Jolla, California, United States

Q46829000 - Salk Institute picks a new president: scientific article

Q46138406 - Salk Institute president to step down: scientific article

Q46331003 - Salk Institute president is replaced: scientific article published in December 1994

Mars,_Inc.

Q135047694 - 2007: video game developed by Hit'n'Run Digital Studio

Q53389304 - 2007-2008 one-year-period: period of about one year starting in 2007 and ending in 2008

Q15729072 - 2007 Census of the Philippines: item representing census of Philippines 2007 for source

Mars,_Inc.

Mars,_Inc.

Q7987 - computer mouse: hand-held device used to move a pointer on a computer display

Q421110 - micelles: aggregates of molecules held loosely together by secondary bonds

Q1364740 - knockout mouse: genetically modified mouse used in gene research

Mars,_Inc.

Q27875836 - Rats: 1994 studio album by Sass Jordan

Q26018 - rat: several genera of rodents with common names similar to "rat"

Q904538 - Rats & Star: Japanese male blackface pop group that specialized in doo-wop-influenced music

Q5073407 - Rats: 2002 film by Tibor Takács

Mars,_Inc.

Q52722439 - Guinea Pigs: episode of Laverne & Shirley (S2 E14)

Q122934460 - Guinea Pigs: sculpture by William Zorach

Q119020421 - Guinea Pigs: painting by George Morland (1763–1804), Dundee Art Galleries and Museums Collection (Dundee City Council)

Q126078352 - Guinea Pigs: episode of Peppa Pig (S7 E38)

Q40715148 - Guinea pigs: scientific article published on January 1994

Output: None of the candidates

Output: None of the candidates


Child labor and slave labor

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: Mars_Inc, Child labor and slave labor: Mars has been criticized for buying cocoa beans from West African farmers who reportedly use unpaid or poorly paid child laborers. In 2009, Mars announced that the company would work towards only purchasing cocoa from suppliers who meet environmental, labor, and production standards.TransFair USA, an organization which certifies products as Fair Trade, applauded the move and expressed hope that it would include a provision for fair wages for laborers and farmers.In 2010, Mars Inc. received the U.S. Secretary of State's Award for Corporate Excellence.In April 2010, Mars launched the MyCocoaPaper initiative, which claims to provide economic opportunities to women and families inIndonesiaby making paper products out of cocoa bark and recycled office paper. In 2011, Mars andFairtrade Internationalannounced an agreement to introduce the first Fairtrade labeled Mars product and to work together to enable farmers to have sustainable livelihoods and substantially increased productivity. The first Mars product to carry the Fairtrade mark was Maltesers, which appeared in stores in 2012 in the UK and Ireland. In 2019, Mars announced that they could not guarantee that their chocolate products were free from child slave labor, as they could trace only 24% of their purchasing back to the farm level (see below). TheWashington Postnoted that the commitment taken in 2001 to eradicate such practices within 4 years had not been kept, neither at the due deadline of 2005, nor within the revised deadlines of 2008 and 2010, and that the result was not likely to be achieved for 2020 either. In 2021, Mars was named in a class action lawsuit filed by eight former child slaves fromMaliwho alleged that the company aided and abetted their enslavement on cocoa plantations inIvory Coast. The suit accused Mars (along withNestlé,Cargill,Barry Callebaut,Olam International,the Hershey Company, andMondelez International) of knowingly engaging in forced labor, and the plaintiffs sought damages for unjust enrichment, negligent supervision, and intentional infliction of emotional distress.In June 2021, theUnited States Supreme Courtdismissed the lawsuit on the grounds that as the abuse had happened outside the United States, the group did not have standing to file such a lawsuit. Mars scored a yellow rating, the second highest of four possible scores, "Starting to implement good policies", on the 2022 Chocolate Scorecard, which rates all the large chocolate companies on their record in eliminating child labour, providing a living income to cocoa farmers and traceability/transparency.In its own scorecard on human rights and environmental credentials, Mars states that it has traced the source of their cocoa to 132,000 of the farms that supply their cocoa. A CBS television news investigation in 2023 found children as young as five years old working in the Ghana supply chain of Mars to harvest cocoa for brands such as Snickers and M&Ms.

[
    "Q695087 - Mars, Incorporated: American global food company and manufacturer",
    "Q134584046 - Mars, Inc.: 2013 novel by Ben Bova"
]

[
    "Q2716518 - Maltesers: confectionery product manufactured by Mars",
    "Q130560104 - Malteserstraße: street in Dormagen, North Rhine-Westphalia, Germany",
    "Q72940809 - Malteserstraße: street in Berlin-Marienfelde and Berlin-Lankwitz, Germany",
    "Q15715637 - Malteserstraße: street in Königswinter, Germany",
    "Q1887838 - Malteserstöckl, Stroheim: None"
]

[
    "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."
]

[
    "Q14213 - United States Secretary of State: head of the United States Department of State",
    "Q113667974 - United States Secretary of State John Foster Dulles Visits Israel: newsreel",
    "Q133815082 - United States Secretary of State Marco Rubio at the NATO Foreign Ministers Meeting April 2025: speech given at NATO HQ, Brussels, Belgium (April 3, 2025)",
    "Q66488805 - United States Secretary of State in the Constitutional History——From the Perspective of Political Constitution: article published in 2018"
]

[
    "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"
]

[
    "Q117 - Ghana: country in West Africa",
    "Q503623 - Gold Coast Colony: former British colony (1867-1957), now Ghana",
    "Q172014 - Ghana men's national football team: men's national association football team representing Ghana",
    "Q42891 - Krishna: major deity in Hinduism, worshipped as the eighth avatar of Vishnu and also as the Supreme god in his own right",
    "Q5555368 - Ghana Medical Journal: journal"
]

[
    "Q519711 - Fairtrade Labelling Organizations International: group promoting a living income for farmers"
]

[
    "Q695087 - Mars, Incorporated: American global food company and manufacturer",
    "Q134584046 - Mars, Inc.: 2013 novel by Ben Bova"
]

[
    "Q912 - Mali: country in West Africa",
    "Q11987935 - Mali: female given name",
    "Q187255 - rheumatoid arthritis: autoimmune disease which attacks healthy cells and tissue located in joints",
    "Q172341 - ovarian cancer: female reproductive organ cancer that is located in the ovary",
    "Q1148337 - hepatocellular carcinoma: liver carcinoma that has material basis in undifferentiated hepatocytes"
]

[
    "Q166032 - The Washington Post: daily broadsheet newspaper in Washington, D.C.",
    "Q106501824 - Washington Post: Vanity Fair caricature of The Rt Hon Sir HM Durand by Spy",
    "Q7773643 - The Washington Post Writers Group: American syndication service",
    "Q76313739 - Washington Post 2011 data breach: None",
    "Q19055540 - Washington Post Company v. Chaloner: None"
]

[
    "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"
]

[
    "Q714179 - Snickers: brand name chocolate bar made by Mars, Incorporated",
    "Q886003 - Bluemont: unincorporated village in Loudoun County, Virginia, United States",
    "Q1748734 - Snickers Workwear: clothing brand",
    "Q18450971 - Hultafors Group AB: None",
    "Q4405719 - Snickers salad: US dessert dish of Snickers bars, apples, and whipped cream"
]

[
    "Q739550 - M&M's: brand of chocolate candy pieces",
    "Q28752866 - Presidential M&M's: commemorative packs of red, white, and blue-colored M&M's given to guests of the president of the United States on board Air Force One"
]

[
    "Q117 - Ghana: country in West Africa",
    "Q503623 - Gold Coast Colony: former British colony (1867-1957), now Ghana",
    "Q172014 - Ghana men's national football team: men's national association football team representing Ghana",
    "Q42891 - Krishna: major deity in Hinduism, worshipped as the eighth avatar of Vishnu and also as the Supreme god in his own right",
    "Q5555368 - Ghana Medical Journal: journal"
]

[
    "Q49628 - 2021: year",
    "Q19241643 - 2021: 2021 is a natural number between 2020 and 2022",
    "Q110427963 - Polish census of 2021: None",
    "Q60745365 - Australian Census 2021: 18th census of the population of Australia",
    "Q135024621 - 2021: 2021 video game developed by 3000Studio"
]

[
    "Q11201 - Supreme Court of the United States: highest court of jurisdiction in the United States",
    "Q19692072 - United States Supreme Court decision: decision rendered by the highest court of the United States",
    "Q1579670 - United States Supreme Court Building: seat of the Supreme Court of the United States of America",
    "Q20706330 - United States Supreme Court justice: Chief Justice and associate justice of the Supreme Court of the United States",
    "Q27717198 - United States Supreme Court Reports, Lawyers' Edition: legal reporter of United States Supreme Court published by Lexis"
]

[
    "Q2783852 - class action: type of lawsuit",
    "Q1168375 - Class Action: 1991 film by Michael Apted",
    "Q2975701 - Class Action: disco and post-disco musical projects",
    "Q111371987 - Class Action: None",
    "Q107339807 - Class Action: poem by Demetria Martinez"
]

[
    "Q5430724 - Fairtrade Mark Ireland: None",
    "Q99309202 - Fairtrade-märkta livsmedelsprodukter: written question from Jonas Jacobsson Gjörtler to Per Bolund"
]

[
    "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"
]

[
    "Q1008 - Ivory Coast: sovereign state in West Africa",
    "Q175145 - Ivory Coast men's national football team: national association football team",
    "Q1544423 - Ivory Coast women's national football team: women's national association football team representing Ivory Coast",
    "Q3589950 - Ivory Coast Olympic football team: national association football team",
    "Q1675853 - Ivory Coast national rugby union team: national rugby union team of Ivory Coast"
]

[
    "Q43380 - CBS: American broadcast television and radio network",
    "Q1043319 - Israel Central Bureau of Statistics: Israel's principal government institution in charge of statistics and census data",
    "Q167086 - Statistics Netherlands: Netherlands' principal government institution in charge of statistics and census data",
    "Q3234345 - Cajal body: class of nuclear body enriched in small nuclear ribonucleoproteins",
    "Q921907 - Charles Bonnet syndrome: visual disturbances and the experience of complex visual hallucinations in a blind person"
]

[
    "Q1988 - 2001: year",
    "Q841792 - 2001: natural number",
    "Q182280 - 2001: 1999 studio album by Dr. Dre",
    "Q103474 - 2001: A Space Odyssey: 1968 film by Stanley Kubrick",
    "Q10806 - September 11 attacks: 2001 attacks in the United States"
]

[
    "Q2019 - 2005: year",
    "Q841374 - 2005: natural number",
    "Q428598 - 2005 United Kingdom general election: election of members to the House of Commons in 2005",
    "Q53385836 - 2005-2006 one-year-period: period of about one year starting in 2005 and ending in 2006",
    "Q520673 - Mazda RX-8: rotary powered sports car manufactured by Japanese automobile manufacturer Mazda from 2003–2012"
]

[
    "Q2004 - 2008: year",
    "Q1069941 - 2008: natural number",
    "Q8567 - 2008 Summer Olympics: Games of the XXIX Olympiad, in Beijing, China",
    "Q135106232 - 2008: vocal track by Annika Norlin; 2008 studio recording",
    "Q130758128 - 2008: exhibition at the Jewish Museum, Amsterdam, The Netherlands (2007)"
]

[
    "Q25274 - 2019: year",
    "Q12681717 - 2019: natural number",
    "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"
]

[
    "Q1994 - 2011: year",
    "Q851312 - 2011: natural number",
    "Q42501043 - 2011 Census data  Andhra Pradesh - Villages dataset: 2011 census data Andhra Pradesh - Villages (dataset)",
    "Q1861882 - 2011 census of India: 15th Indian census",
    "Q4350770 - 2011 Population and Housing Census: census in Estonia in 2011"
]

[
    "Q252 - Indonesia: island country in Southeast Asia and Oceania",
    "Q9240 - Indonesian: official language of Indonesia",
    "Q155214 - Indonesian Wikipedia: Indonesian-language edition of Wikipedia",
    "Q112136164 - Indonesian Minister of Home Affairs Decree Number 050-145 of 2022: Decree of the Minister of Home Affairs of the Republic of Indonesia Number 050-145 of 2022 concerning the Granting and Updating of Codes, Government Administration Area Data, and Islands in 2021",
    "Q109692162 - Indonesian 2009 DPR Ballot: None"
]

[
    "Q1995 - 2010: calendar year",
    "Q851662 - 2010: natural number",
    "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"
]

Output: None of the candidates