gliner-linker-base-v1.0

pip install git+https://github.com/Knowledgator/GLinker.git

from glinker import ConfigBuilder, DAGExecutor

# Build pipeline
builder = ConfigBuilder(name="entity_linking")

# L1: Extract mentions
builder.l1.gliner(
    model="knowledgator/gliner-bi-base-v2.0",
    labels=["person", "organization", "location"]
)

# L2: Candidate retrieval
builder.l2.add("dict", priority=0)

# L3: Disambiguation with GLiNER-Linker
builder.l3.configure(
    model="knowledgator/gliner-linker-large-v1.0",
    use_precomputed_embeddings=True
)

# Execute
executor = DAGExecutor(builder.get_config())
executor.load_entities("entities.jsonl", target_layers=["dict"])

result = executor.execute({
    "texts": ["Apple announced new iPhone"]
})

# Get linked entities
l0_result = result.get("l0_result")
for entity in l0_result.entities:
    if entity.linked_entity:
        print(f"{entity.mention_text} → {entity.linked_entity.label}")
        print(f"  Score: {entity.linked_entity.score:.3f}")