entity-resolution-strategy-selector

Entity Resolution Strategy Selector

Overview

Entity resolution determines when two data records refer to the same real-world entity — the cornerstone that lets an agent maintain a coherent worldview across fragmented systems. If the agent cannot decide when two references are the same entity, its whole reasoning framework collapses: the graph either conflates distinct entities or fragments a single one.

The chapter draws one decisive distinction, and this skill turns it into a selection:

• Evidence-based resolution examines specific features, applies domain-specific matching rules, and builds a case from concrete evidence. It is deterministic (same input, same output), explainable (every match cites which features drove it and their scores), culturally robust (explicit rules handle Arabic / Chinese / Russian naming), and calibrated (confidence reflects actual match accuracy).
• Generalization-based AI (an LLM) infers from statistical similarity learned in training. It is nondeterministic, its explanations are post-hoc rationalizations, it breaks on non-Western names, and its confidence is not tied to accuracy.

Evidence-based wins for identity, compliance, high-stakes, and adversarial work. The sharp case is channel separation: a money launderer appears as Bob Jones, then Bob R. Smith II at the same address with different phone formatting, then Robert Smith Jr. elsewhere with overlapping contact details — each variation engineered to pass fuzzy filters while looking distinct. Simple string matching fails catastrophically; what wins is consolidating fragmented identities on evidence from multiple overlapping features.

The selector scores a requirement profile across the six factors the chapter names (high_stakes, adversarial channel-separation, explainability, determinism, cultural_variation, training_examples) and returns evidence-based, generalization-AI, or a hybrid (LLM for cheap candidate generation, evidence-based for the auditable final decision). The matcher makes the trade-off concrete: it scores name/address/phone similarity, aggregates to an explainable confidence with per-feature evidence metadata (the chapter's "89% because NAME 87%, ADDRESS 100%, PHONE 95%"), classifies the graph edge (RESOLVED / POSSIBLY_RELATED / DISCLOSED), and flags the three edge cases that require domain and cultural knowledge.