An LLM-driven triple-stage prompt for automatic disassembly knowledge graph construction of end-of-life power batteries towards industry 5.0

Song, Lifang, Du, Zhenjie, Yan, Wei and Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940 2026. An LLM-driven triple-stage prompt for automatic disassembly knowledge graph construction of end-of-life power batteries towards industry 5.0. Industries 1 (1) , 6. 10.3390/industries1010006

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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.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Schools > Engineering
Publisher:	MDPI
Date of First Compliant Deposit:	27 July 2026
Date of Acceptance:	20 July 2026
Last Modified:	27 Jul 2026 11:30
URI:	https://orca.cardiff.ac.uk/id/eprint/188465

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