  Facts extraction is pivotal for constructing knowledge graphs. Recently, the
increasing demand for temporal facts in downstream tasks has led to the
emergence of the task of temporal fact extraction. In this paper, we
specifically address the extraction of temporal facts from natural language
text. Previous studies fail to handle the challenge of establishing
time-to-fact correspondences in complex sentences. To overcome this hurdle, we
propose a timeline-based sentence decomposition strategy using large language
models (LLMs) with in-context learning, ensuring a fine-grained understanding
of the timeline associated with various facts. In addition, we evaluate the
performance of LLMs for direct temporal fact extraction and get unsatisfactory
results. To this end, we introduce TSDRE, a method that incorporates the
decomposition capabilities of LLMs into the traditional fine-tuning of smaller
pre-trained language models (PLMs). To support the evaluation, we construct
ComplexTRED, a complex temporal fact extraction dataset. Our experiments show
that TSDRE achieves state-of-the-art results on both HyperRED-Temporal and
ComplexTRED datasets.
