We present Large Temporal Model, a Large Language Model (LLM) that excels in Temporal Relation Classification (TRC). We show how a carefully designed fine-tuning strategy, using a novel two-step fine-tuning approach, can adapt LLMs for TRC. Our approach is focused on global TRC, enabling simultaneous classification of all temporal relations within a document. Unlike traditional pairwise methods, our approach performs global inference in a single step, improving both efficiency and consistency. Evaluations on the MATRES and OmniTemp benchmarks demonstrate that, for the first time, an LLM achieves state-of-the-art performance, outperforming previous pairwise and global TRC methods. Results show that our global approach produces more consistent and accurate temporal graphs. Ablation studies further validate the effectiveness of our two-step fine-tuning strategy, while analyses reveal why our approach succeeds in increasing performance and reducing inconsistencies.