Dear Editors-in-Chief and Guest Editors of the X-NeSy Special Issue, We are pleased to submit our manuscript, "Operability as Structural Grounding: Conditions for Epistemic Appropriation in LLMs," for consideration in the Special Issue on Explainable Neurosymbolic AI (X-NeSy). In line with the call, we confirm that this submission is intended for the X-NeSy special issue. Our paper addresses a question at the heart of this special issue: if Large Language Models necessarily hallucinate, making confabulation a mathematical consequence of statistical inference, under what conditions can such a model produce outputs whose correctness is not merely asserted, but transparently grounded and verifiable? We introduce Operability, a framework specifying three jointly necessary conditions under which an LLM can epistemically appropriate its own outputs. We validate the framework empirically on maze-solving and Answer Set Programming scheduling tasks (using the Clingo solver). A cross-model analysis yields a result we believe is of independent interest: on extended-thinking models the effect does not disappear once accuracy saturates but re-surfaces as a path-efficiency gap that closes only under external execution, confirming that extended internal reasoning does not substitute for the externally closed verification loop. The work engages directly with the faithfulness of model reasoning and with the use of formal, symbolic methods to certify neural behavior — themes central to the call's "Formal Verification for Explainability" and "Generating Structured Explanations" topics. The manuscript is original, has not been previously published, and is not under consideration for publication elsewhere. It is co-authored by the corresponding author and Paolo Ciancarini (University of Bologna). We thank the editors for their consideration and look forward to engaging with the journal's open and transparent review process. Sincerely, Andrea Gasparro on behalf of the authors