## 1 Introduction
### Research Motivation
Although the introduction of LLMs into KGC tasks has significantly enhanced the ability of models to complete missing triples, there still exist two major limitations that need to be addressed. On the one hand, existing CoT prompting mechanisms often lack explicit constraints derived from the underlying structure of the knowledge graph. As a result, the generated reasoning chains may deviate from the true topology of the graph, thereby limiting the effective exploitation of structured knowledge. A parallel issue can be observed in the cognitive process of structured knowledge visualization. [PERSON] et al. [18], through eye-tracking experiments, demonstrated that visual representations of structural information significantly enhance learners' reasoning accuracy in mathematical concept comprehension. This cognitive evidence indirectly highlights the critical role of structural guidance in complex knowledge reasoning and provides theoretical support for the introduction of Graph-CoT prompting in KGC tasks. On the other hand, current LLMs lack a dedicated confidence evaluation mechanism when generating tail entities, making it difficult to quantify the uncertainty of model outputs. This shortcoming adversely affects the stability and controllability of predictions. To address these challenges, researchers have explored the integration of structured information into CoT-prompted LLMs to enhance reasoning performance. For example, KG-LLM encodes multi-hop paths into natural language prompts to guide LLMs in simulating chain-of-thought reasoning. However, it lacks effective constraints on path selection and entity re-ranking, often resulting in structural drift during the reasoning process. DIFT [19] introduces graph compression and logical control mechanisms to improve output reliability, yet its reasoning chain construction remains relatively simplistic and struggles to model complex structural dependencies accurately. Moreover, most existing LLM-based approaches do not incorporate quantitative evaluation of model outputs. In light of the above limitations, we propose GLR, a novel framework that integrates Graph Chain-of-Thought (Graph-CoT) [20] prompting with low-overhead adaptation of LLMs through the LoRA technique [21]. In addition, GLR incorporates a confidence evaluation mechanism based on P(True) [22] to effectively constrain the generated results, thereby enhancing the reasoning ability of LLMs and improving the reliability of predictions in KGC tasks. Specifically, GLR first constructs graph structure-aware reasoning prompts by selecting related triples from the KG that have a common head entity or relation with the query triple, forming Graph-CoT [20] prompts that enable the model to perform stepwise reasoning over the candidate entity set under the guidance of graph structure. Then, based on the Graph-CoT prompts, GLR designs an instruction fine-tuning task [16; 23] and applies LoRA [21] to enable lightweight adaptation on the base model Qwen2-7B [24], enhancing its adaptability to domain-specific knowledge. Finally, GLR introduces a P(True)-based confidence evaluation mechanism [22], which guides the model to assess the confidence of the predicted triples by appending binary judgment prompts. The confidence scores are then used to rank the candidate entities, improving the reliability of the final predictions [25].