Multimodal Table Question Answering requires both accurate perception of cell contents in a table image and precise symbolic reasoning over the extracted values; multimodal large language models (MLLMs) handle the former well, but the latter remains more challenging, motivating the use of an explicit code interpreter for computation. Through a fine-grained partition analysis, we find instance-level complementarity between the two paths: even when the visual path is stronger overall, the structured path correctly answers a non-trivial subset of questions missed by the visual path. To exploit this complementarity, we propose TriVTab, a dual-path framework that combines direct visual reasoning with triple-based structured reasoning through agreement-gated multimodal fusion. Rather than seeking a uniformly stronger standalone path, TriVTab reconciles complementary paths to improve a strong visual baseline. TriVTab (i) uses an execution-oriented cell-triple representation to bind each value to an explicit row and column context, making complex table structures directly addressable for code generation; and (ii) employs agreement-gated multimodal fusion to treat cross-path agreement as corroboration and concentrate multimodal fusion on divergent predictions. On five multimodal Table QA benchmarks, TriVTab improves over a strong Qwen3-VL-8B chain-of-thought baseline by +2.53 points on average and over a Self-Reflection baseline by +3.46 points.