Diffusion large language models (dLLMs) generate text through iterative denoising, but they remain prone to factual errors in knowledge-intensive tasks. Through layer-wise and timestep-wise probing, we observe a spatiotemporal stratification pattern in dLLMs: structural-token predictions tend to converge earlier across layers, whereas factual-token predictions rely more heavily on deep layers and become reliable only after sufficient denoising progress. Motivated by this observation, we propose Time-Aware Internal Contrastive Decoding (TICD), a training-free decoding method that calibrates final-layer predictions using entropy-gated intermediate-layer references only during semantically reliable denoising stages. This design uses reliable final-intermediate prediction gaps as correction signals to strengthen factual predictions at the final layer. Experiments on LLaDA-8B and Dream-7B across factuality and reasoning benchmarks show consistent improvements over vanilla decoding.