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manylives.me Named Entity Recognition
x402
manylives.me Named Entity Recognition is a paid API for AI agents from manylives.me, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-13).
Extracts named entities (people, organizations, places, dates, money amounts, products) from text, each with its type and frequency count.
Price / call$0.01
Success—
StatusUnknown
How this service works
Named entities in a text: people, organisations, places, dates, money amounts, products and other notable items, each with its type and how many times it appears.
Output
A JSON array of entity objects, each containing the entity's text (e.g. 'Acme Pty Ltd'), its semantic type (e.g. 'organisation', 'person', 'place', 'date', 'money', 'product'), and the number of times it appears in the input text.
Example use cases
Entity extraction from news article
“Here's a news article about a merger — can you pull out all the companies, people, and dollar amounts mentioned, and tell me how many times each one appears?”
Contract party identification
“I have this contract text. Can you find all the people, organisations, and places named in it, along with how many times each one shows up?”
Research document entity audit
“I need to know which organisations and individuals are referenced most in this research report — can you extract all named entities and give me a count for each?”
Technical details
Endpoint
POSThttps://manylives.me/paid/text/entities?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to identify and count specific types of named entities (people, organizations, places, dates, money, products) from arbitrary text, particularly when frequency/co-occurrence matters. It is purpose-built for NER with typed classification, unlike generic text extraction or summarization tools. Ideal for document analysis, compliance checks, or knowledge graph population where entity type and frequency are both needed.
Side effects
- Text exceeds 20,000 character limit — request rejected
- Empty or whitespace-only text — may return empty entity list
- Ambiguous entities (e.g. a word that could be a person or place) may be misclassified
- Non-English or mixed-language text may reduce extraction accuracy
- Very short or context-free text may yield low-confidence or incomplete results
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | the text (required, max 20,000 chars) |
Output
Schema · Response
{
"type": "json",
"example": {
"entities": [
{
"text": "Acme Pty Ltd",
"type": "organisation",
"count": 2
}
]
}
}Tags
x402
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