Most post-hoc explainability methods for graph classification analyze the model’s internal representations rather than explicitly capturing its reasoning process. These approaches typically rely on perturbations, gradients, or optimization techniques to infer important features but do not approximate the decision-making function itself. In this paper, we propose a novel approach that directly models the GNN’s decision function using a Transparent Explainable Logic Layer (TELL). This logic-based approximation enables both instance-level and global-level explanations, offering insights into how node embeddings contribute to predictions. Unlike conventional methods, our approach derives explanations that are structurally aligned with the model’s decision process rather than being externally imposed. Through experiments on synthetic and real-world graph classification tasks, we show that our method produces faithful, sparse, and stable explanations, outperforming existing techniques.