## 5 Results  
### Main Results  
To evaluate the effectiveness of the proposed GLR method, we compare it against a variety of representative baseline models, including embedding-based methods and LLM-based methods. Table 2 summarizes the KGC performance (MRR and Hits@\(K\)) of different models on the FB15K-237 and WN18 RR datasets [47], while Table 3 presents the corresponding results on the UMLS dataset, which focuses on the medical knowledge graph domain. The standard deviations are obtained from three runs with different random seeds. As shown in Table 2, on the more challenging FB15K-237 dataset, GLR achieves a competitive MRR of 0.507, representing a 9.2% improvement over the well-established embedding-based model NBFNet (MRR = 0.415), and a 6.8% gain over the recent LLM-based method DIFT \(+\) CoLE. In addition, GLR attains a Hits@10 of 0.643, outperforming competitive baselines such as NBFNet (Hits@10 = 0.599), indicating its improved ability to retrieve correct answers. These results demonstrate that the integration of Graph-CoT reasoning and the candidate filtering strategy effectively enhances the model's capacity to identify accurate results, contributing to both better ranking quality and higher recall under the Top-K setting. On the WN18 RR dataset, GLR attains a competitive MRR of 0.679, outperforming recent strong baselines such as SimKGC (MRR = 0.671) and DIFT (MRR = 0.617). This represents a relative improvement of 0.8% over SimKGC, the best-performing baseline in this metric. However, it does not obtain the highest scores in Hits@3 and Hits@10, which may be attributed to the characteristics of WN18 RR. Specifically, WN18 RR has a relatively sparse KG structure with fewer relation types, which is less favorable for methods requiring complex reasoning chains, such as Graph-CoT employed in GLR. In such scenarios, theadvantages of multi-hop reasoning approaches may not be fully realized. Nevertheless, GLR maintains leading performance in overall prediction accuracy on WN18 RR due to its comprehensive design, demonstrating strong reasoning capability. It is also worth mentioning that some zero-shot LLM-based methods (e.g., directly using ChatGPT [48] or LLaMA [49] for reasoning) often suffer from outputting invalid entities that are not included in the candidate set due to the lack of task-specific constraints. This typically leads to lower Hits@\(K\) performance. In contrast, GLR adopts candidate generation and confidence-based filtering strategies to ensure that the output is always restricted within a reasonable candidate space, thereby significantly improving the valid hit rate and avoiding interference from irrelevant answers. Table 3 further presents the experimental results on the UMLS dataset, which focuses on the medical domain. It can be observed that all models achieve relatively high performance on this dataset. GLR achieves an MRR of 0.804, representing a new strong baseline in this domain. Compared to the best CP-KGC-based result (MRR = 0.798), GLR achieves a relative improvement of 0.6% in MRR. In addition, GLR reaches a Hits@1 of 0.715, outperforming the strongest baseline (0.678) by 3.7%, which demonstrates the framework's enhanced ability to accurately identify the top-ranked tail entity. GLR also maintains competitive results on Hits@(3, 10), achieving 0.893 and 0.962, respectively; while not the highest across all metrics, these scores reflect the model's stable ranking capability and reinforce its overall robustness on the UMLS. The advantage of GLR lies in its ability to not only leverage the semantic knowledge encoded in pre-trained language models but also explicitly incorporate graph structure information for chain-of-thought reasoning. As a result, GLR is able to achieve subtle yet consistent improvements even when the evaluation metrics on this dataset are close to saturation.