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Tanod mlpeek Named Entity Recognition
x402
Tanod mlpeek Named Entity Recognition is a paid API for AI agents from tanod.dev, paid per call via x402, $0.001/call, status unknown (last checked 2026-10-12).
Extracts named entities with character offsets from English text using spaCy's OntoNotes label set (18 entity types).
Price / call$0.001
Success—
StatusUnknown
How this service works
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Output
Returns a JSON object containing: a list of entity objects (each with text, label, start offset, end offset), per-label entity counts, total characters analyzed, model info (en_core_web_sm-3.8.0, MIT license), number of processing chunks, and a flag indicating if entities were truncated. Offsets are Unicode code-point indices, end-exclusive.
Example use cases
Extract entities from news article
“Here's a news article snippet — can you identify all the people, organizations, locations, and dates mentioned in it, along with their exact character positions? Text: 'Google CEO Sundar Pichai announced a $2 billion investment in France on Monday, partnering with the European Union.'”
Filter company names from press release
“Scan this press release for only company and organization names using NER: 'Microsoft, OpenAI, and Nvidia joined forces with the Department of Defense in Washington to launch a new AI initiative last Tuesday.' Just give me the ORG entities with their offsets.”
Build structured event timeline from text
“I have this paragraph describing a series of events — can you extract all the DATE and EVENT entities with their character positions so I can build a timeline? Text: 'The Treaty of Lisbon was signed on 13 December 2007. The financial crisis of 2008 followed shortly after, reshaping global markets.'”
Technical details
Endpoint
POSThttps://tanod.dev/v1/ner?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, structured NER with exact character offsets on English text using the OntoNotes 18-label taxonomy (including CARDINAL, NORP, FAC, WORK_OF_ART, LAW, etc.), especially when you want to filter to specific entity types. Prefer over general LLM-based extraction when reproducibility, offset precision, and low per-call cost matter. Best for texts up to 20,000 characters.
Side effects
- Text exceeds 20,000 character limit — request rejected
- Empty or missing text field — validation error
- Invalid label value in labels array — validation error
- Non-English text provided — model still runs but accuracy is significantly degraded
- Very short or punctuation-only text — returns empty entities list
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | English text, at most 20,000 characters. |
| labels | — | Only these entity types (default: all 18 OntoNotes types). |
Output
Schema · Response
{
"type": "json",
"example": {
"note": "offsets are Python string indices (Unicode code points), end exclusive; English model (OntoNotes labels)",
"model": {
"id": "en_core_web_sm-3.8.0",
"source": "explosion/spacy-models (en_core_web_sm)",
"license": "MIT",
"revision": "3.8.0"
},
"chunks": 1,
"counts": {
"GPE": 1,
"ORG": 1,
"DATE": 1,
"MONEY": 1,
"PERSON": 1
},
"entities": [
{
"end": 5,
"text": "Apple",
"label": "ORG",
"start": 0
},
{
"end": 23,
"text": "$3.5 million",
"label": "MONEY",
"start": 11
},
{
"end": 37,
"text": "John Smith",
"label": "PERSON",
"start": 27
},
{
"end": 47,
"text": "Berlin",
"label": "GPE",
"start": 41
},
{
"end": 65,
"text": "5 October 2026",
"label": "DATE",
"start": 51
}
],
"chars_analyzed": 66,
"entities_truncated": false
}
}Tags
x402
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