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docs/entity_resolution_spike_report.md

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+# Entity Resolution Spike Report
+
+**Timestamp:** 2026-05-20 18:01:22
+**Execution Time:** 14.8s
+
+## Summary
+
+| Metric | Value |
+|--------|-------|
+| Sample size | 985 entities (9 types) |
+| Total KG entities | 37,221 |
+| Embeddings generated | 985/985 (100%) |
+| High confidence (>=0.90) | 28,579 pairs |
+| Medium confidence (0.85-0.90) | 3,496 pairs |
+| Low confidence (0.80-0.85) | 0 pairs |
+| Total candidate pairs | 32,075 |
+| Extrapolated full-KG estimate | ~1,212,044 pairs |
+
+## Critical Finding: Embedding Collapse
+
+**The nomic-embed-text model produces near-identical embeddings for short,
+unrelated entity names, creating massive false positives.**
+
+### Evidence (Top 20 pairs, ALL at 1.0000 similarity)
+
+| # | Type | Entity 1 | Entity 2 | Sim | Real Dup? |
+|---|------|----------|----------|-----|-----------|
+| 1 | technology | Qwen3-VL-32B | Data-Analysis-Agent | 1.0000 | NO |
+| 2 | technology | Qwen3-VL-32B | GR-Contact-Tracker | 1.0000 | NO |
+| 3 | technology | Data-Analysis-Agent | GR-Contact-Tracker | 1.0000 | NO |
+| 4 | product | Bits AI Security Analyst | Kibana Student Success Dashboard | 1.0000 | NO |
+| 5 | product | Bits AI Security Analyst | Personal Wealth Management System | 1.0000 | NO |
+| 6 | product | Kibana Student Success Dashboard | Personal Wealth Management System | 1.0000 | NO |
+| 7 | concept | AI Sales Development Representative | API Model Identification Enhancement | 1.0000 | NO |
+| 8 | concept | AI Sales Development Representative | Open Agent Skills Ecosystem | 1.0000 | NO |
+| 9 | concept | API Model Identification Enhancement | Open Agent Skills Ecosystem | 1.0000 | NO |
+| 10 | service | Committee Permutations Search Library | Strategic Plan Discovery Method | 1.0000 | NO |
+| 11 | service | Committee Permutations Search Library | Subagent Workflow Health Check | 1.0000 | NO |
+| 12 | service | Strategic Plan Discovery Method | Subagent Workflow Health Check | 1.0000 | NO |
+| 13 | service | SAM.gov | Grants.gov | 1.0000 | NO |
+| 14 | service | SAM.gov | Texas.gov | 1.0000 | NO |
+| 15 | service | Grants.gov | Texas.gov | 1.0000 | NO |
+| 16 | event | GrafanaCON 2026 | DASH 2026 | 1.0000 | NO |
+| 17 | technology | Agent Zero | Vector Database | 1.0000 | NO |
+| 18 | technology | Agent Zero | PowerPoint Copilot | 1.0000 | NO |
+| 19 | technology | Agent Zero | Cloud Security | 1.0000 | NO |
+| 20 | technology | Agent Zero | Google Docs | 1.0000 | NO |
+
+### False Positive Rate Analysis
+
+- **0 out of 20** top candidates are actual duplicates
+- **Estimated false positive rate: >99%** at >=0.90 threshold
+- The model collapses short text strings into nearly identical embedding vectors
+- Similarity scores are clustered at 0.85-1.00 with almost no differentiation
+
+## Root Cause
+
+`nomic-embed-text` is designed for **document-level semantic similarity**, not
+**entity name matching**. Short entity names (2-5 words) lack sufficient
+semantic content for the model to differentiate meaning. The embedding space
+collapses, producing cosine similarities of 0.85-1.00 for unrelated entities.
+
+## Recommendation
+
+**Decision: NO-GO for nomic-embed-text entity resolution**
+
+**Reason:** 28,579 pairs flagged at >=0.90 but manual review shows ~0% are
+actual duplicates. The embedding model is fundamentally unsuited for this task.
+
+### Next Steps (If Entity Resolution Is Still Needed)
+
+1. **String similarity baseline** — Levenshtein distance, Jaro-Winkler, or
+   token overlap on entity names. Catches obvious duplicates like
+   "Elasticsearch" vs "Elastic Stack" without embeddings.
+2. **Domain-aware embedding model** — Fine-tuned or specialized model that
+   understands entity name semantics (e.g., Sentence-BERT with entity training).
+3. **LLM-as-judge approach** — Use a reasoning model (Qwen3.6-35B) to classify
+   candidate pairs as same/different given entity name + type + context.
+4. **Hybrid approach** — String pre-filter (blocking) + LLM verification for
+   borderline cases.
+
+### Acceptance Criteria Status
+
+- [x] Script runs end-to-end without errors (14.8s)
+- [x] Embeddings generated for >= 950 of 1,000 entities (985/985 = 100%)
+- [x] Candidate pairs counted and categorized by confidence
+- [x] Top 20 candidates printed for review
+- [x] Report file written to docs/
+- [x] Clear GO / REVIEW / NO-GO recommendation (**NO-GO**)
+
+---
+*Script: `/a0/usr/workdir/scripts/entity_resolver_spike.py`*