Quantitative metallography requires reliable boundary-derived measurement. Prior computational pipelines established feasibility, but reliability gaps remain when weak and interrupted interfaces are analyzed under crop-based workflows. Here, we study how context, supervision, and image representation jointly affect boundary fidelity. First, using a context-ablation experiment, we show that removing global context sharply degrades boundary identifiability, indicating that context is a first-order condition for reliable interface detection. As a methodological response, we introduce MLOGRAPHY++, a context-preserving partial-label approach that aligns supervision with annotation ambiguity while reducing dependence on heavy post-processing. We then test whether improving representation fidelity can further improve quantitative reliability when boundary evidence is visually weak, using controlled diffusion-based super-resolution under fixed prompting, matched-resolution controls, matched detector training, and expert audit. Under these controls, discrimination metrics remain similar, while Heyn-based grain-size error decreases from 9.98 px to 5.38 px. Together, these results support a bounded interpretation in the evaluated benchmark setting and show that reliable quantitative metallography benefits from coordinated attention to context, supervision, and image representation.