Large knowledge graphs face a critical challenge: as millions of entities and relationships accumulate, determining whether "Sony Corp" in a new document matches existing nodes becomes computationally expensive. Traditional ingestion pipelines resort to costly global searches, degrading performance while struggling with semantic ambiguities across naming variations.

Proxy-Pointer RAG addresses this by treating vector databases as structural indexes rather than fragmented chunk stores. Unlike standard RAG that splits documents into context-free snippets, Proxy-Pointer preserves document hierarchy through skeleton trees and breadcrumb injection. This allows retrieved chunks to serve as pointers to complete sections, giving synthesizer LLMs full context for accurate entity extraction.

The approach combines five zero-cost techniques: hierarchical document parsing, structural path prepending, boundary-respecting chunking, noise filtering, and pointer-based context retrieval. Testing with AMD's 10-K filings demonstrates how this method efficiently reconciles entities like "Sony" with their correct business roles—platform owner versus chip supplier—without expensive graph traversal.

For enterprises managing sprawling knowledge graphs, Proxy-Pointer offers a practical solution to control entity proliferation while maintaining ingestion performance. The technique shifts reconciliation workload from costly graph queries to faster vector retrieval, making large-scale document processing more sustainable.