Deforestation in African national parks

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, Deforestation in African national parks: In September 2017, an investigationconducted by NGO Mighty Earth found that a large amount of the cocoa used in chocolate produced by Mars and other major chocolate companies was grown illegally innational parksand other protected areas inIvory CoastandGhana.The countries are the world's two largest cocoa producers. The report documents show, in several national parks and other protected areas, 90% or more of the land mass has been converted to cocoa.Less than four percent of Ivory Coast remains densely forested, and the chocolate companies' laissez-faire approach to sourcing has driven extensivedeforestationin Ghana as well.In Ivory Coast, deforestation has pushed chimpanzees into just a few small pockets, and reduced the country's elephant population from several hundred thousand to about 200–400. On the 2022 Chocolate Scorecard, Mars scored the second highest of four possible scores for "Deforestation and Climate" and "Agroforestry", "starting to implement good policies". Mars scored a lower rating (third of the four) for "Agrochemical Management", "needs more work on policy and implementation".

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["Mars_Inc", "Awards"], ["Mighty_Earth", "Awards"], ["Agrochemical_Management", "Awards"], ["Ivory_Coast", "Awards"], ["Ghana", "A


Deforestation in African national parks

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, Deforestation in African national parks: In September 2017, an investigationconducted by NGO Mighty Earth found that a large amount of the cocoa used in chocolate produced by Mars and other major chocolate companies was grown illegally innational parksand other protected areas inIvory CoastandGhana.The countries are the world's two largest cocoa producers. The report documents show, in several national parks and other protected areas, 90% or more of the land mass has been converted to cocoa.Less than four percent of Ivory Coast remains densely forested, and the chocolate companies' laissez-faire approach to sourcing has driven extensivedeforestationin Ghana as well.In Ivory Coast, deforestation has pushed chimpanzees into just a few small pockets, and reduced the country's elephant population from several hundred thousand to about 200–400. On the 2022 Chocolate Scorecard, Mars scored the second highest of four possible scores for "Deforestation and Climate" and "Agroforestry", "starting to implement good policies". Mars scored a lower rating (third of the four) for "Agrochemical Management", "needs more work on policy and implementation".

[["Mars_Inc", "has_product_line", "Chocolate"], ["Mars_Inc", "launched_campaign", "\"Deforestation and Climate\""], ["Mars_Inc", "launched_campaign", "\"Agrochemical Management\""], ["Mars_Inc", "has_product_line", "Cocoa"], ["Mars_Inc", "launched_campaign", "\"Agroforestry\""], ["Mars_Inc", "has_product_line", "Mars"]], "2022_Chocolate_Scorecard": [["Mars_Inc", "score", "\"Agroforestry\""], ["Mars_Inc", "score", "\"Deforestation and Climate\""], ["Mars_Inc", "score", "\"Agrochemical Management\""]]