{"uid":"cap_gyZwhxWjDcFSA-VKIe-Re","slug":"primeapi-ner-named-entity-recognition-cc9b88e0","name":"PrimeAPI NER — Named Entity Recognition","description":"Crypto, AI, Social Media, Finance, Weather, Tools & more. Pay per call in USDC on Base. No API keys.","url":"https://prime-api-seven.vercel.app/api/ai/ner","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"query":{"type":"string","description":"Search query or input parameter"}}},"responseSchema":{"type":"object","description":"API response data"},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.140649","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":"$0.140649/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.140649","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.140649","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_g5CzLCWPIbhSxLkmfDk-8","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.140649","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, etc.) from a text query using NLP","exampleAgentPrompt":"Extract all the named entities from this text and tell me what type each one is: 'Apple CEO Tim Cook announced a new partnership with Goldman Sachs in New York last Tuesday.'","exampleUseCases":[{"title":"Extract entities from social media posts","prompt":"I've got a batch of tweets about tech companies I need to analyze. Can you pull out all the people, organizations, and locations mentioned in each one so I can track who's talking about what?"},{"title":"Identify contacts in customer support tickets","prompt":"Our support team gets hundreds of messages daily. Help me automatically extract all the person names and company names from incoming tickets so we can route them to the right department."},{"title":"Parse locations and dates from travel queries","prompt":"Users keep sending us travel requests with scattered information like 'meeting Jane in London next Friday then going to Tokyo.' Can you extract just the names, places, and dates so I can structure this into a booking system?"}],"resultDescription":"A structured response containing the identified named entities found in the input text, with each entity labeled by its type (e.g., PERSON, ORG, GPE, DATE) and its position or value within the original text.","failureModes":["Empty or missing query field returns an error","Text too long may be truncated or rejected","Ambiguous entities may be misclassified or omitted","Non-English text may yield poor or no entity extraction","Generic or very short inputs may return no entities","Payment failure (402) if USDC balance is insufficient"],"whenToPreferThis":"Choose this endpoint when you need fast, pay-per-call NER without managing API keys or ML infrastructure. Ideal for agents that need to extract structured entity data from arbitrary text snippets on demand, especially when cost predictability per-call matters and integration with Base/USDC payment flows is already in place.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T00:49:09.308Z","isFirstParty":false}