llm-extraction-kg-construction

name	LLM Extraction & KG Construction
description	Core inference logic, prompt construction, and RDF/Nanopublication serialization.

Instructions

You are interacting with the src/knowledge_graph/ module. This handles the transition from natural language text to structured assertions via Large Language Models.

Agentic Interface (MCP Strategy)

1. Config-Driven Orchestration: Never hardcode LLM parameters (models, temperature, minimum confidence thresholds) within Python scripts. Bind them tightly to LLMConfig structures derived securely from config.yaml.
2. Robust Categorization: Respect strict boundaries on confidence gating. When crafting or modifying prompt templates, ensure the extraction schema firmly permits "irrelevant", "neutral", and "don't know" states to neutralize base-model hallucination tendencies.
3. Streaming Persistence: Assertions must emit safely to disk via incremental JSON Lines or valid TriG records. High-volume document processing should not accumulate global lists in RAM.