## 2 Related Work
### LLM-Based  
With the continuous advancement of LLMs in natural language reasoning tasks [4], researchers have started to explore their potential for KGC [30]. Existing LLM-based methods can be broadly categorized into two groups: prompt-based and fine-tuning-based [31].  
Prompt-based methods do not update model parameters but guide LLMs to perform reasoning by constructing examples and context prompts [32; 33]. KICGPT [14] proposes a context-enhanced KG reasoning framework, which first generates a top-m candidate entity set using a pre-trained KGE model. It then encodes the query triple and candidate entities into a prompt to guide ChatGPT (gpt-3.5-turbo) in predicting tail entities through multi-turn question answering. The final entity completion is accomplished through re-ranking the candidate entities according to the generated content, thereby eliminating the need for additional fine-tuning and exhibiting strong generalization capabilities in both few-shot [34] and zero-shot prediction settings [35].  
Fine-tuning paradigms improve LLMs' adaptability to KG structures and their structured reasoning ability through parameter updates. KG-LLM [15] models the KGC task as a natural language question-answering task, linearizing multi-hop triple paths into language reasoning chains using CoT prompts [13], guiding the model to predict tail entities within multi-hop relational contexts [36], and leveraging LLMs' language reasoning capabilities to enhance the integration of multi-hop path information and entity prediction tasks. KoPA [37] focuses on enhancing LLMs' perception of KG structures by introducing a structural prefix adapter mechanism, which compresses pre-trained KGE information into vector form and injects it into the model input as prefix prompts, helping the model perceive structural patterns and relational distributions among entities, thereby improving its performance on entity completion tasks. DIFT [19] emphasizes controllability and discriminative ability in model outputs, combining discriminative instruction prompt design to guide the model in selecting entities from a pre-filtered candidate set. It employs LoRA [21] for parameter-efficient fine-tuning of LLMs, balancing accuracy and training efficiency, and effectively enhancing the stability of entity prediction. In addition, the recently proposed KnowLA framework [38] further demonstrates the applicability of LoRA in knowledge-enhanced fine-tuning. By introducing a structure-guided adaptation module, it enhances structural alignment and generalization during the fine-tuning process. These findings confirm the feasibility of integrating structural awareness with lightweight adaptation and provide both theoretical and technical foundations for the GLR framework proposed in this study.  
Overall, these fine-tuning paradigms have advanced LLMs' adaptability to KGC tasks from different perspectives, including structure awareness, path modeling, and controlled entity selection, laying the foundation for subsequent research on more fine-grained structure modeling and confidence evaluation mechanisms. In this paper, we propose the GLR framework, which enhances LLMs' structural perception and output controllability while preserving their general capabilities. By integrating the structured Graph-CoT [20] prompting and the P(True)-based confidence evaluation mechanism [22], GLR further complements and extends existing fine-tuning methods in structured reasoning and result reliability.