  Temporal Knowledge Graph Completion (TKGC) is a complex task involving the
prediction of missing event links at future timestamps by leveraging
established temporal structural knowledge. This paper aims to provide a
comprehensive perspective on harnessing the advantages of Large Language Models
(LLMs) for reasoning in temporal knowledge graphs, presenting an easily
transferable pipeline. In terms of graph modality, we underscore the LLMs'
prowess in discerning the structural information of pivotal nodes within the
historical chain. As for the generation mode of the LLMs utilized for
inference, we conduct an exhaustive exploration into the variances induced by a
range of inherent factors in LLMs, with particular attention to the challenges
in comprehending reverse logic. We adopt a parameter-efficient fine-tuning
strategy to harmonize the LLMs with the task requirements, facilitating the
learning of the key knowledge highlighted earlier. Comprehensive experiments
are undertaken on several widely recognized datasets, revealing that our
framework exceeds or parallels existing methods across numerous popular
metrics. Additionally, we execute a substantial range of ablation experiments
and draw comparisons with several advanced commercial LLMs, to investigate the
crucial factors influencing LLMs' performance in structured temporal knowledge
inference tasks.
