Why Batch Pipelines Fail Corporate Knowledge Graphs and How to Fix Them

A developer building 2asy.ai, a Graph-RAG system for East Asian corporate intelligence, discovered that standard batch pipelines break down when applied to incrementally updated corporate knowledge graphs. The core problem is that batch-based entity resolution relies on blocking keys across an entire document corpus, meaning new incoming data cannot be matched against existing graph nodes, leading to duplicate disconnected entries. A second issue arises when company details change over time, as naive append logic stores both old and new values simultaneously, causing conflicting facts that confuse downstream language models. The proposed fixes include a live-graph entity resolution pattern that checks incoming entities against existing nodes before insertion, and a time-bounded fact assertion model that supersedes outdated property values rather than overwriting or duplicating them. Both solutions are demonstrated with Python and Neo4j code examples designed for production incremental update pipelines.