A three-model pipeline
with zero connector code

# NERBertAdapter.egress() — the only code you write
def egress(self, output, original_ir, latency_ms):
    updated = original_ir.copy()
    entities, cur, toks, scores = [], None, [], []
    for token in output:
        tag, word, sc = token["entity"], token["word"], token["score"]
        if tag.startswith("B-"):
            if cur: entities.append({
                "text": "".join(toks).replace("##",""),
                "label": cur,
                "confidence": sum(scores)/len(scores)
            })
            cur, toks, scores = tag[2:], [word], [sc]
        elif tag.startswith("I-"): toks.append(word); scores.append(sc)
    updated.payload.entities = entities
    updated.provenance.append(self.build_provenance(
        confidence=max(e["confidence"] for e in entities),
        latency_ms=latency_ms
    ))
    return updated

# ClassifierAdapter.ingress() — reads from canonical IR
def ingress(self, ir):
    return [{
        "text":         e["text"],
        "entity_type":  e["label"],   # label → entity_type
        "context_window": ir.payload.content[:80],
        "threshold":   ir.task_header.quality_floor or 0.7,
    } for e in (ir.payload.entities or [])]