Retrieval-augmented generation (RAG) improves factual question answering by grounding LLMs in external evidence, but it becomes brittle when retrieved context conflicts with the parametric memory of the model. Existing training-free methods often address this problem by globally comparing two knowledge sources or structurally aligning them, yet they still struggle with three coupled failure axes: question complexity, long misleading evidence, and off-target parametric recall. We first quantitatively analyze the individual and joint effects of these three axes, showing that their interaction makes knowledge conflict resolution harder than any single axis alone. Motivated by this finding, we propose TRIAD, a training-free framework for knowledge conflict resolution in RAG. TRIAD decomposes a question into minimal subquestions, performs subquestion-level evidence calibration to retain concise and sufficient evidence, and uses indirect clues from calibrated evidence to guide parametric knowledge elicitation. Across three benchmarks and six LLMs, TRIAD achieves state-of-the-art performance and is the only method that surpasses a setting with additional retrieval in most cases. Additional experiments further demonstrate its generalization and worst-case robustness.