{"uid":"cap_HTxc6vEuKpiF5Wtx_oMTL","slug":"agishub-named-entity-extraction-c83186bd","name":"AGISHub Named Entity Extraction","description":"Extract named entities from text — people, organizations, locations, dates and miscellaneous — returned as structured JSON.","url":"https://api.agishub.com/v1/extract","method":"GET","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input","output"],"properties":{"input":{"type":"object","required":["type","method","queryParams"],"properties":{"type":{"enum":["http"],"type":"string"},"method":{"enum":["GET"],"type":"string"},"queryParams":{"type":"object","required":["text"],"properties":{"text":{"type":"string","minLength":1,"description":"The text to extract named entities from."}},"additionalProperties":false}},"additionalProperties":false},"output":{"type":"object","required":["type","example"],"properties":{"type":{"enum":["json"],"type":"string"},"example":{"type":"object"}},"additionalProperties":false}}},"responseSchema":{"type":"json","example":{"result":"Extract named entities from text — people, organizations, locations, dates and miscellaneous — returned as structured JS"}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.02","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.02/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.02","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.02","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_hpIXGOPaDm05mhojlXMdN","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.02","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Extracts named entities (people, organizations, locations, dates, and miscellaneous) from a given text and returns them as structured JSON.","exampleAgentPrompt":"Can you pull out all the named entities — people, organizations, locations, and dates — from this text: 'Apple CEO Tim Cook met with French President Emmanuel Macron in Paris on June 10, 2024 to discuss AI regulation.'","exampleUseCases":[{"title":"News article entity tagging","prompt":"I have this news article snippet and I need to know all the people, companies, and places mentioned in it: 'Elon Musk's SpaceX launched a Falcon 9 rocket from Cape Canaveral, Florida on behalf of NASA yesterday.' Can you extract all the named entities from it?"},{"title":"Contract party identification","prompt":"Pull out every organization and person named in this contract excerpt: 'This agreement is entered into between Acme Corp, represented by John Smith, and GlobalTech Inc, represented by Maria Gonzalez, effective January 1, 2025.'"},{"title":"Social media mention extraction","prompt":"I'm analyzing this tweet for a report and need to identify every named entity in it — people, locations, orgs, dates, anything: 'Serena Williams attended the Wimbledon gala in London last Saturday alongside UNICEF representatives.'"}],"resultDescription":"A structured JSON object containing named entities grouped by category — people, organizations, locations, dates, and miscellaneous — extracted from the input text.","failureModes":["Empty or whitespace-only text input returns an error or empty entity list","Very short text with no proper nouns may return empty results","Ambiguous entity types may be miscategorized (e.g. a person's name mistaken for a location)","Non-English text may produce lower-quality or incomplete extraction","Rate limiting or payment failure returns a 402 error"],"whenToPreferThis":"Choose this endpoint when you need fast, structured named entity recognition (NER) over arbitrary text and want results categorized into people, organizations, locations, dates, and miscellaneous — especially in agentic pipelines where a pay-per-call model is preferred over running a self-hosted NLP model. It is a good fit for document enrichment, news analysis, and content tagging use cases.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T18:47:37.248Z","isFirstParty":false}