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Interpretability is widely claimed for graph neural networks (GNNs) in metallurgy and alloy property prediction. Yet the term remains ambiguous: authors’ frequently present attention maps that highlight atoms or bonds, then conclude that the model has uncovered physical mechanisms governing strength, ductility, or creep resistance. This boundary/definitional article demonstrates that such claims conflate three fundamentally different concepts—statistical explainability, mechanistic interpretability, and causal interpretability—none of which is automatically satisfied by attention weights or feature-importance scores. Attention maps reveal which inputs the model attends to, but they do not establish why those inputs matter, nor do they predict the outcome of microstructural interventions. Drawing exclusively on the peer-reviewed literature that applies GNNs to metallurgical systems, the article distinguishes statistical correlations captured by attention mechanisms from the physical mechanisms required for trustworthy alloy design. It proposes an operational, multi-level definition of interpretability tailored to metallurgy, specifying validation criteria for each level. Statistical explainability (Level 1) is shown to be the weakest form, limited to correlational insights; mechanistic interpretability (Level 2) reveals internal model computations; and causal interpretability (Level 3) demands experimental or simulated interventions to confirm counterfactual predictions. Attention maps do not equal physical mechanisms. The framework clarifies the boundary between useful visualizations and scientifically actionable knowledge, offering concrete reporting standards for model developers, materials scientists, and journal editors. Adoption of these distinctions will accelerate the transition from black-box property prediction to models that genuinely advance metallurgical understanding and enable physics-informed alloy design.