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The reliability of machine-learned (ML) interatomic potentials in phase space sampling depends critically on whether sampled configurations lie within the interpolative domain of the training data or extend into extrapolative regimes. Despite its central importance, the interpolation–extrapolation distinction remains inconsistently defined across the literature. Existing single-metric approaches—such as convex-hull composition checks, descriptor-space distances, uncertainty estimates, and local-environment similarity—capture only partial aspects of high-dimensional configuration space and frequently yield conflicting classifications. This lack of a rigorous, unified boundary undermines active learning strategies, uncertainty quantification, benchmarking, and the safe deployment of ML potentials in safety-critical applications. This boundary/definitional article introduces a unified and operational framework that delineates the interpolation–extrapolation boundary through four independent and jointly necessary criteria: (C1) compositional coverage, (C2) local-environment similarity, (C3) configurational co-occurrence, and (C4) thermodynamic condition. A configuration is defined as interpolative only when all four criteria are simultaneously satisfied; violation of any criterion constitutes extrapolation. To replace binary classification, the framework further introduces a graded hierarchy of extrapolation—mild, moderate, and severe—based on the number and magnitude of violations. The proposed definitions are deliberately operational, relying exclusively on information available during training-set construction or simulation runtime, and are agnostic to model architecture. Their adoption enables standardized reporting, stratified benchmarking, improved calibration of uncertainty estimates, and more targeted active-learning workflows. By establishing a clear and reproducible boundary, the framework provides a principled foundation for evaluating model reliability during phase space exploration. This work advances the epistemic rigor of ML-driven materials modeling and supports the responsible and transparent deployment of data-driven interatomic potentials.