# `Arcana.Graph.EntityExtractor.NER`
[🔗](https://github.com/georgeguimaraes/arcana/blob/main/lib/arcana/graph/entity_extractor/ner.ex#L1)

Extracts named entities from text using Bumblebee NER.

Uses dslim/distilbert-NER to identify persons, organizations,
locations, and miscellaneous entities. The model is lazy-loaded
on first use to avoid startup overhead when graph features aren't needed.

## Usage

    # As configured extractor
    config :arcana, :graph,
      entity_extractor: :ner

    # Direct usage
    {:ok, entities} = Arcana.Graph.EntityExtractor.NER.extract(text, [])

# `extract`

Extracts entities from text using the NER model.

Returns a list of entity maps with :name, :type, :span_start, :span_end, :score.
Entities are deduplicated by name (first occurrence kept).

## Examples

    iex> NER.extract("Sam Altman is CEO of OpenAI.", [])
    {:ok, [
      %{name: "Sam Altman", type: "person", span_start: 0, span_end: 10, score: 0.99},
      %{name: "OpenAI", type: "organization", span_start: 22, span_end: 28, score: 0.98}
    ]}

# `extract_batch`

Extracts entities from multiple texts.

## Examples

    iex> NER.extract_batch(["Sam Altman", "Elon Musk"], [])
    {:ok, [[%{name: "Sam Altman", ...}], [%{name: "Elon Musk", ...}]]}

# `map_label`

Maps NER labels to entity types.

## Label Mapping
- PER, B-PER, I-PER → "person"
- ORG, B-ORG, I-ORG → "organization"
- LOC, B-LOC, I-LOC → "location"
- MISC, B-MISC, I-MISC → "concept"
- Other → "other"

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*Consult [api-reference.md](api-reference.md) for complete listing*