GraphRAG Looks Great Until Entity Resolution Breaks
Vector RAG is great at answering "what does X mean?" It's terrible at answering "how is X related to Y through Z?"
GraphRAG was built to solve this. But it introduces a failure mode that's harder to detect and more destructive than anything in vanilla RAG: entity disambiguation errors that compound exponentially.
Where Vector RAG Breaks
Vector search maps your query to semantically similar document chunks. It works well for flat, single-hop lookups:
"What's our refund policy?" → Retrieves the refund policy chunk → Correct answer
It breaks on anything requiring structural reasoning:
"Which upstream services are affected by the failing auth cluster?"
A vector database retrieves chunks mentioning individual services. But the dependency chain between them — which service calls which, the order of failure propagation — is shattered during chunking. The model is forced to hallucinate the connections.
Vector RAG gives you isolated facts. GraphRAG gives you connected knowledge.
How GraphRAG Works
GraphRAG extracts entities and relationships from your documents, building a knowledge graph of explicit nodes and edges. When you query it, the system traverses the graph topology instead of just measuring embedding distance.
The model receives a constrained, factual subgraph — not a pile of loosely related text chunks. This drastically reduces hallucination on relational queries.
The Trap: Entity Disambiguation
Here's the problem nobody warns you about.
When you extract entities from thousands of documents, you encounter "Dr. Smith" 847 times. Is it the same person? Three different people? The chief medical officer in document A and the external consultant in document B?
This is the entity resolution problem, and if your accuracy drops below ~85%, the entire knowledge graph becomes toxic.
Why Errors Compound
In a knowledge graph, a single misidentified entity doesn't just produce one wrong answer — it poisons every path that traverses through it.
Query: "What safety concerns are associated with Drug Trial X?"
- Correct graph: Returns only directly associated safety data
- Corrupted graph: Traverses through the merged "Dr. Smith" node, pulling in Safety Report Y which is about a completely unrelated study
The error multiplies with each hop. A 15% entity resolution error rate doesn't mean 15% of answers are wrong — it means every multi-hop query has a (0.85)^n chance of being correct, where n is the number of hops.
| Hops | Accuracy at 95% Entity Resolution | Accuracy at 85% Entity Resolution |
|---|---|---|
| 1 | 95% | 85% |
| 2 | 90% | 72% |
| 3 | 86% | 61% |
| 5 | 77% | 44% |
At 85% entity resolution and 5 hops, fewer than half your answers are trustworthy.
The Triple-Index Challenge
Production GraphRAG isn't just a graph database. You need three indexes synchronized in real-time:
- Text Index — raw document chunks for full-text search
- Vector Index — embeddings for semantic similarity
- Graph Index — entities and relationships for structural traversal
Keeping these three indexes consistent as documents are added, updated, or deleted is a data engineering nightmare. Every document change must propagate to all three stores, and the entity resolution step must re-evaluate whether new mentions merge with existing entities or create new ones.
The Architecture That Works: Agentic Graph RAG
The production-ready pattern is an intent-based routing layer that uses the right retrieval strategy for each query:
| Query Type | Routing Decision | Example |
|---|---|---|
| Simple factual | Vector search | "What's our vacation policy?" |
| Keyword-specific | Text search | "Find all mentions of HIPAA" |
| Relational / causal | Graph traversal | "How does Team A's output feed into Team B's pipeline?" |
| Complex / ambiguous | Hybrid: vector → graph | "What risks are associated with Project Apollo's dependencies?" |
The router itself can be a lightweight classifier or a small LLM call that categorizes query intent before dispatching to the appropriate index. This avoids the latency and cost of hitting all three indexes on every query.
Practical Advice
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Start with Vector RAG — it covers 70%+ of real queries. Only add GraphRAG when users consistently ask relational questions that vector search can't answer.
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Invest in entity resolution before scaling the graph — a small, accurate graph beats a large, noisy one. Use human-in-the-loop validation for critical entity types.
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Monitor graph health metrics — track entity merge rates, orphan node counts, and traversal error rates. Set alerts when resolution accuracy drops.
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Don't build all three indexes on day one — start with vector + a simple graph overlay. Add the text index when search requirements demand it.
GraphRAG is powerful. But the engineering cost is real, and entity disambiguation is where most teams silently fail.
Building a production RAG system? I've navigated the vector-to-graph transition — let's talk at hello@sowmith.dev