{"uid":"cap_eldJGifJCnnVCxSCtWzSv","slug":"named-entity-extraction-people-orgs-places-products-dates-money-tickers-e9677287","name":"Named-Entity Extraction: People, Orgs, Places, Products, Dates, Money, Tickers","description":"Named-entity extraction: people, orgs, places, products, dates, money, tickers — Genesis402 / UnyKorn Operator Network","url":"https://twin.unykorn.org/ai/entities?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"params":{"type":"object","properties":{"text":{"type":"string","description":"required, up to 16,000 chars"}}}}},"responseSchema":{"type":"json","example":{"ok":true,"type":"text-entities","receipt":{"tx_hash":"0x<64hex>","amount_usd":0.004,"receipt_id":"g402-<16hex>"},"sources":[{"ok":true,"name":"<source>"}],"limitations":"<text>","generated_at":"<iso time>","evidence_hash":"sha256:<64hex>"}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.004","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":"$0.004/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.004","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.004","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_9KQ23N1eoEGZ-xxx3GGql","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.004","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, places, products, dates, monetary values, and stock tickers) from up to 16,000 characters of input text.","exampleAgentPrompt":"Pull out all the named entities from this paragraph — I need the people, companies, locations, dates, dollar amounts, and any stock tickers mentioned: 'Apple Inc. CEO Tim Cook announced on Monday that the company would invest $430 billion in the US over the next five years, with major expansions in Austin, TX.'","exampleUseCases":[{"title":"Entity extraction from earnings call transcript","prompt":"Extract all the named entities from this earnings call transcript — I want every company name, executive name, product mentioned, dollar figure, and date: 'Amazon CFO Brian Olsavsky said Q3 revenue reached $143.1 billion, driven by AWS growth. The company plans to expand its Kuiper satellite service in Europe by late 2025.'"},{"title":"Named entities from news article for knowledge graph","prompt":"Parse this Reuters article and pull out every person, organization, location, date, and monetary value so I can feed them into our knowledge graph builder."},{"title":"Ticker and financial entity extraction from analyst note","prompt":"I have an analyst note full of stock tickers, company names, and price targets — can you extract all the financial entities from it? Here's the text: 'We upgrade NVDA to Buy with a $950 target. Microsoft (MSFT) and Alphabet (GOOGL) remain Hold-rated given $2.1B capex headwinds.'"}],"resultDescription":"Returns a structured list of named entities found in the input text, categorized by type: people, organizations, places, products, dates, monetary values, and stock tickers. Each entity is labeled with its category and the text span as it appeared in the source.","failureModes":["Text exceeding 16,000 characters may be rejected or truncated","Ambiguous abbreviations (e.g. 'Apple' as fruit vs company) may be misclassified","Low-quality or highly informal text may yield missed or incorrect entities","Payment failure via x402 protocol will prevent endpoint access","Very short or context-free text may produce low-confidence extractions"],"whenToPreferThis":"Choose this endpoint when you need to identify and categorize multiple named entity types — especially the combination of financial entities (tickers, money) alongside standard NER types (people, orgs, places) — from a single text input. Ideal for financial document parsing, news article enrichment, knowledge graph population, or any workflow requiring structured entity extraction from free text up to 16,000 characters.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-02T01:21:09.841Z","isFirstParty":false,"canonicalSlug":"named-entity-extraction-people-orgs-places-products-dates-money-tickers-e9677287"}