{"uid":"cap_JxU3YlIjlEO4TYby5WDUm","slug":"x402-ai-tools-named-entity-recognition-5dab72a1","name":"x402 AI Tools – Named Entity Recognition","description":"Finds named entities (person, location, organization, commercial item, event, date, quantity, title, other) in a text with Amazon Comprehend, with their offsets and scores. Supports en, es, fr, de, it, pt, ar, hi, ja, ko, zh, zh-TW; the language is detected when not given.","url":"https://ai.kagurazaka.dev/v1/entities?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"text":{"type":"string","description":"the text, up to 5000 bytes of UTF-8"},"language":{"type":"string","description":"language code (en, es, fr, de, it, pt, ar, hi, ja, ko, zh, zh-TW); detected when omitted"}}},"responseSchema":{"type":"json","example":{"entities":[{"end":22,"text":"Acme","type":"organization","begin":18,"score":0.98},{"end":39,"text":"Tokyo","type":"location","begin":34,"score":0.99}],"language":"en","language_detected":true}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.008","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":"$0.008/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.008","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.008","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_L5HpAquInZ4YDFlY1Zkgx","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.008","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, locations, organizations, dates, etc.) with character offsets and confidence scores from text using Amazon Comprehend, with optional language auto-detection.","exampleAgentPrompt":"Find all named entities — people, organizations, locations, dates, and anything else — in this text and tell me their types, positions, and confidence scores: 'Acme Corp signed a deal in Tokyo on March 15 with CEO Jane Smith.'","exampleUseCases":[{"title":"Extract entities from news article","prompt":"I have this news paragraph and I need to pull out every person, organization, location, and date mentioned along with where they appear in the text: 'The United Nations convened in Geneva last Tuesday, where Secretary-General António Guterres met with EU officials.' Can you identify all named entities with their offsets and confidence scores?"},{"title":"Tag entities in Spanish customer feedback","prompt":"This customer review is in Spanish — can you extract all named entities from it, including any brands, locations, and people mentioned? The text is: 'El presidente de Samsung, Lee Jae-yong, visitó la planta de Madrid el lunes pasado.'"},{"title":"Analyze entities in legal document excerpt","prompt":"I need to identify every organization, person, and date in this contract excerpt so I can build a knowledge graph. Here's the text: 'On January 10 2024, Apex Holdings LLC agreed with Dr. Richard Ito of Boston Biotech to license the patent.' Please return each entity with its type, position, and score."}],"resultDescription":"A JSON object containing an array of entity objects, each with: the matched text span, entity type (organization, person, location, date, event, commercial item, quantity, title, or other), begin and end character offsets, and a confidence score (0–1). Also includes the language code used and a boolean indicating whether language was auto-detected.","failureModes":["Text exceeds 5000 bytes — request rejected or truncated","Unsupported language code provided — error response","Empty or whitespace-only text — returns empty entities array","Language detection fails for very short or ambiguous text — may default or error","Network/service timeout — HTTP 5xx response","Payment failure via x402 protocol — 402 Payment Required if USDC payment not completed"],"whenToPreferThis":"Choose this endpoint when you need structured, span-level named entity annotations (with character offsets and type labels) from multilingual text, backed by Amazon Comprehend's production-grade NER models. Prefer it over generic LLM prompting for NER when you need reliable confidence scores, consistent entity type taxonomy, and exact character positions — especially for non-English languages (12 supported). It is cost-effective at $0.008 per call for texts up to 5000 bytes.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-30T12:32:22.776Z","isFirstParty":false,"canonicalSlug":"x402-ai-tools-named-entity-recognition-5dab72a1"}