## 6 Discussion  
This study systematically evaluates the applicability and effectiveness of the proposed GLR framework across multiple KGC tasks, demonstrating significant advantages in structural modeling, reasoning accuracy, and model efficiency. Experimental results show that GLR effectively enhances the predictive capabilities of medium-scale LLMs, particularly exhibiting greater stability and generalization in multi-hop reasoning and structurally complex scenarios. Ablation studies further verify the synergistic benefits of the framework's three core components: Graph-CoT prompting, LoRA fine-tuning, and P(True) confidence ranking. Specifically, structural prompts facilitate semantic path alignment, parameter-efficient tuning reduces computational overhead, and the confidence mechanism significantly improves the controllability and trustworthiness of model outputs. Moreover, GLR maintains robust decision-making performance even under conditions of limited training data or constrained candidate sets, indicating its practical potential in low-resource scenarios. Collectively, these findings validate the effectiveness and research value of integrating structure-guided reasoning with confidence modeling to enhance LLM-based KGC performance. In response to the three research questions (RQs) posed in this study, we provide the following answers:  
**RQ1**: Can structural information be introduced to enhance LLMs' ability to model contextual structure? **Answer**: Our findings suggest that knowledge graphs often contain rich structural information that has been underutilized in prior work, which limits model performance. To address this issue, we design the Graph-CoT prompting mechanism to guide the model  
Figure 8: Cross-domain transfer results. in performing structure-aware chain-of-thought reasoning, thereby compensating for the structural modeling limitations of conventional CoT methods. **RQ2**: How can large models be trained to leverage Graph-CoT capabilities? **Answer**: We integrate Graph-CoT with LoRA-based fine-tuning, utilizing LoRA's instruction-following capabilities to improve model alignment and performance. This is achieved with only a small number of trainable parameters, making the approach suitable for deployment in resource-constrained environments while minimizing computational cost. **RQ3**: How can we improve the reliability of predictions and mitigate hallucination in LLMs? **Answer**: We propose the P(True) confidence evaluation mechanism, which estimates confidence scores for candidate entities without requiring additional models. This approach effectively reduces generative errors and improves output consistency. Comparative experiments with other uncertainty estimation methods confirm its advantage in balancing predictive reliability with inference efficiency. Despite the strong performance of the GLR framework on several mainstream KGC tasks, it faces notable limitations when applied to ultra-large-scale knowledge graphs characterized by highly imbalanced relation distributions or sparse structural connectivity. Specifically, when certain relation types appear infrequently in the training data, the Graph-CoT prompts generated by GLR may lack sufficient structural context. This impairs the model's ability to construct meaningful reasoning chains and reduces prediction accuracy. In addition, cross-domain transfer remains a significant challenge. As demonstrated in the experimental results in Section 5.8, the model exhibits considerable performance degradation when applied to domains not encountered during training, particularly when substantial semantic or structural discrepancies exist between knowledge graphs. Furthermore, in large-scale knowledge graphs such as Wikidata, which contain millions of entities and relations, the sparsity and complexity of multi-hop paths can lead to failures in path retrieval or token length overflow during structured prompt generation. These factors intensify the model's reliance on graph structure and increase the risk of prediction bias.