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Named-Entity Extraction: People, Orgs, Places, Products, Dates, Money, Tickers
x402
Named-Entity Extraction: People, Orgs, Places, Products, Dates, Money, Tickers is a paid API for AI agents from twin.unykorn.org, paid per call via x402, $0.004/call, status unknown (last checked 2026-10-02).
Extracts named entities (people, organizations, places, products, dates, monetary values, and stock tickers) from up to 16,000 characters of input text.
Price / call$0.004
Success—
StatusUnknown
How this service works
Named-entity extraction: people, orgs, places, products, dates, money, tickers — Genesis402 / UnyKorn Operator Network
Output
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.
Example use cases
Entity extraction from earnings call transcript
“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.'”
Named entities from news article for knowledge graph
“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.”
Ticker and financial entity extraction from analyst note
“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.'”
Technical details
Endpoint
POSThttps://twin.unykorn.org/ai/entities?utm_source=zero.xyzWhen to prefer
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.
Side effects
- 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
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| params | object |
Tags
x402
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