{"uid":"cap_XG4mPfyBGoppBd5X9UzMC","slug":"netintel-entity-extract-api-5debcddb","name":"NetIntel Entity Extract API","description":"Extract named entities from text using Claude Haiku — people, organizations, locations, dates, emails, URLs, money amounts, and products — returned as structured typed arrays so agents can pull structured signals out of unstructured text in one call.","url":"https://netintel.dev/entity-extract","method":"POST","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input"],"properties":{"input":{"type":"object","required":["type","method","bodyType","body"],"properties":{"body":{"type":"object","required":["text"],"properties":{"text":{"type":"string","description":"The text to extract named entities from — an article, email, document, or any unstructured text. Max 10000 words or 50KB."},"types":{"type":"array","items":{"type":"string"},"description":"Optional subset of entity types to extract: person, organization, location, date, email, url, money, product, phone. Defaults to all types."}}},"type":{"type":"string","const":"http"},"method":{"enum":["POST"],"type":"string"},"bodyType":{"enum":["json","form-data","text"],"type":"string"}},"additionalProperties":false},"output":{"type":"object","required":["type"],"properties":{"type":{"type":"string"},"example":{"type":"object","properties":{"grade":{"type":"string","description":"Letter grade A-F"},"score":{"type":"number","description":"Quality score 0-100"},"entities":{"type":"object","description":"Per-type arrays of unique entity strings as they appear in the text. Only requested types appear when the types param is supplied."},"findings":{"type":"array","items":{"type":"object"},"description":"Informational findings (e.g. extraction errors)"},"total_entities":{"type":"number","description":"Total count across all entity arrays"},"types_extracted":{"type":"array","items":{"type":"string"},"description":"The entity types included in this response"}}}}}}},"responseSchema":{"type":"json","example":{"grade":"A","score":100,"entities":{"url":["https://acme.com"],"date":["2024-03-15"],"email":["jane@acme.com"],"money":["$1.2M"],"phone":[],"person":["Jane Doe","John Smith"],"product":["WidgetPro"],"location":["San Francisco"],"organization":["Acme Corp"]},"findings":[],"total_entities":9,"types_extracted":["person","organization","location","date","email","url","money","product","phone"]}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.05","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":"$0.05/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.05","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.05","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_dukULRqpdxRn7tmTD4ny6","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.05","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Extracts named entities (persons, organizations, locations, dates, emails, URLs, money, products, phone numbers) from text and returns a graded structured result","exampleAgentPrompt":"Extract all named entities from this press release text — I need the people, organizations, locations, dates, money amounts, emails, URLs, and products mentioned: 'Acme Corp CEO Jane Doe announced a $1.2M partnership with WidgetPro in San Francisco on March 15, 2024. Contact jane@acme.com or visit https://acme.com for details.'","exampleUseCases":null,"resultDescription":"Returns a JSON object with an overall grade (e.g. 'A') and numeric score (0-100), a breakdown of extracted entities grouped by type (person, organization, location, date, email, url, money, product, phone), a list of findings, a total entity count, and the list of entity types that were extracted.","failureModes":["Empty or very short text input may return zero entities with a low score","Text in unsupported languages may yield incomplete extraction","Ambiguous or poorly formatted text may misclassify entity types","Network or payment failure via x402 micropayment results in 402 Payment Required","Malformed POST body returns 400 Bad Request"],"whenToPreferThis":"Choose this endpoint when you need a graded, multi-type named entity extraction from free-form text in a single call, especially when you want people, orgs, locations, dates, emails, URLs, money, and products all identified together with a quality score. Prefer this over generic NLP libraries when you need a pay-per-call SaaS solution with no setup, or when building pipelines that need entity richness scoring alongside raw extraction.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-13T12:50:02.693Z","isFirstParty":false}