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.

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