An LLM-Driven Triple-Stage Prompt for Automatic Disassembly Knowledge Graph Construction of End-of-Life Power Batteries Towards Industry 5.0

Abstract

Effective disassembly process planning is fundamental to the sustainability of power battery recycling. However, existing knowledge graph (KG) methods rely on flat ontologies, failing to capture multi-level semantic structures and depending heavily on manual annotation, which cannot meet the scalability demands of Industry 5.0. We propose TSO-Prompt, a triple-stage ontology prompt-driven method. First, a Battery–Component–Operation–Tool (BCOT) ontology model defines four entity types and four relationship types with strict domain and range constraints. Second, a three-stage prompt strategy is designed: Stage 1 (Pattern Injection) embeds BCOT definitions for simultaneous entity recognition, ontology classification, and relation extraction; Stage 2 (Temporal Completion) captures cross-step operational dependencies; Stage 3 (Ontology Self-Check) filters hallucination-induced errors via rule-based verification. The fully zero-shot pipeline requires no manual annotation. Experiments on 172 disassembly steps from five battery models show TSO-Prompt achieves 100% core semantic retention, 90.1% operation recognition accuracy, a 6.4% entity F1 improvement over supervised baselines, and 60% query path length reduction over flat graphs, validating its potential for automated KG construction aligned with Industry 5.0 objectives.