Text

Named Entity Recognition

Identify and extract named entities like people, organizations, and locations

635K runs

Note: This playground provides simulated output to showcase functionality. No input data is processed or stored on our servers. Use this demo to explore the feature extractor's capabilities before integrating it into your application.

Input

Text Input string

Enter the text you want to process

# model string

The NER model to use. Default: en_core_web_lg

# min_confidence number

Minimum confidence threshold for entity detection. Default: 0.8

# entity_types array

Specific entity types to detect. Default: PERSON,ORG,LOC,GPE,DATE,TIME,MONEY,PERCENT,QUANTITY

Output

{
  "entities": [
    {
      "text": "John Smith",
      "type": "PERSON",
      "start": 12,
      "end": 22,
      "confidence": 0.98,
      "normalized_text": "John Smith"
    },
    {
      "text": "Microsoft",
      "type": "ORG",
      "start": 33,
      "end": 42,
      "confidence": 0.95,
      "normalized_text": "Microsoft Corporation"
    },
    {
      "text": "Seattle",
      "type": "LOC",
      "start": 50,
      "end": 57,
      "confidence": 0.99,
      "normalized_text": "Seattle, WA"
    }
  ],
  "model": "en_core_web_lg",
  "language": "en",
  "entity_types": [
    "PERSON",
    "ORG",
    "LOC",
    "GPE",
    "DATE",
    "TIME",
    "MONEY",
    "PERCENT",
    "QUANTITY"
  ]
}

Already have embeddings? Skip extraction — search your own vectors with MVS. First 1M vectors free.

Try MVS →