Information graphs (KGs) are foundational to many AI purposes, however sustaining their freshness and completeness stays pricey. We current ODKE+, a production-grade system that mechanically extracts and ingests thousands and thousands of open-domain details from net sources with excessive precision. ODKE+ combines modular elements right into a scalable pipeline: (1) the Extraction Initiator detects lacking or stale details, (2) the Proof Retriever collects supporting paperwork, (3) hybrid Information Extractors apply each pattern-based guidelines and ontology-guided prompting for giant language fashions (LLMs), (4) a light-weight Grounder validates extracted details utilizing a second LLM, and (5) the Corroborator ranks and normalizes candidate details for ingestion. ODKE+ dynamically generates ontology snippets tailor-made to every entity kind to align extractions with schema constraints, enabling scalable, type-consistent truth extraction throughout 195 predicates. The system helps batch and streaming modes, processing over 9 million Wikipedia pages and ingesting 19 million high-confidence details with 98.8% precision. ODKE+ considerably improves protection over conventional strategies, reaching as much as 48% overlap with third-party KGs and decreasing replace lag by 50 days on common. Our deployment demonstrates that LLM-based extraction, grounded in ontological construction and verification workflows, can ship trustworthiness, production-scale data ingestion with broad real-world applicability.
