## 5 Results
### Impact of Candidate Set Size on GLR Performance  
To evaluate the impact of candidate set size on the performance of the GLR framework, and to further verify the robustness of its structure-aware reasoning capability and confidence evaluation mechanism under different constraint conditions, we design a controlled experiment on the UMLS dataset.  
Unlike the previous experiments, this experiment focuses solely on exploring the effect of candidate set size on model performance. Therefore, for simplicity, we conduct 10 repeated tests for each group of data. Although the accuracy in this setting may be influenced by randomness, it is sufficient for observing the relationship between candidate set size and model performance. Specifically, a subset of entities was stochastically drawn from the complete entity set containing the ground-truth tail entity, and candidate set sizes were configured as Top-10, Top-20, Top-50, and the full entity set. For the first three candidate set sizes, we randomly select the corresponding number of entities from the entire entity set as candidates and repeat the testing process 10 times. In each test, a different candidate set is input into the model for structured question answering, resulting  
Figure 6: Impact of different training corpora on the performance of GLR.  
in 10 different predicted entities. Subsequently, we construct suffix prompts for these 10 candidate entities and calculate their confidence scores using the P(True) mechanism. The entity with the highest confidence score is selected as the final prediction. In the final set of experiments, given the relatively small number of entities in the UMLS dataset, it was feasible to input the entire entity set into the model simultaneously. The experimental results also show that this setting achieves similar performance to our previously proposed batch-wise input strategy designed to handle cases where the number of entities exceeds the maximum token limit of LLMs. This further verifies the effectiveness of our proposed strategy. The experimental results are shown in Figure 7.  
As shown in Figure 7, the size of the candidate set has a significant impact on the prediction performance of GLR. The smaller the candidate set, the better the model performance. In particular, under the Top-10 setting, all evaluation metrics achieve their best values, with Hits@10 reaching 100% accuracy. As the candidate set size increases, the reasoning complexity and semantic interference grow substantially. This results in a more dispersed confidence distribution over the candidate entities, reducing the distinguishability of confidence-based ranking.  
These experimental results indicate that, under the collaborative mechanism of structure-guided reasoning and confidence evaluation, GLR is more adept at making high-quality decisions within small-scale candidate sets. This not only demonstrates the effectiveness of Graph-CoT prompting in constraining the model's reasoning path but also further verifies the practicality and discriminative capability of the P(True)-based confidence evaluation mechanism as a post hoc verifier. In future work, combining lightweight candidate filtering techniques to further compress the candidate space may enhance the reasoning efficiency and performance upper bound of GLR in large-scale open-domain entity environments.