Design a relation extraction pipeline with provenance and canonicalization. Use when you need help with re designer.
Scanned 9/8/2026
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---
name: re-designer
description: Design a relation extraction pipeline with provenance and canonicalization. Use when you need help with re designer.
license: CC-BY-NC-SA-4.0
phase: 5
lesson: 26
metadata:
version: 1.0.0
tags: [nlp, relation-extraction, knowledge-graph]
---
Given a corpus (domain, language, volume) and downstream use (KG-RAG, analytics, compliance), output:
1. Extractor. Pattern-based / supervised / LLM / AEVS hybrid. Reason tied to precision vs recall target.
2. Ontology. Closed property list (Wikidata / domain) or open IE with canonicalization pass.
3. Provenance. Every triple carries source char-span + doc id. Non-negotiable for audit.
4. Merge strategy. Canonical entity id + relation id + temporal qualifiers; dedup policy.
5. Evaluation. Precision / recall on 200 hand-labelled triples + hallucination-rate on LLM-extracted sample.
Refuse any LLM-based RE pipeline without span verification (source provenance). Refuse open-IE output flowing into a production graph without canonicalization. Flag pipelines with no temporal qualifier on time-bounded relations (employer, spouse, position).
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