Weakly-supervised temporal action localization (WS-TAL) remains challenging due to the severe temporal ambiguity inherent in video-level supervision. Existing methods often overfit to highly discriminative snippets, struggling to capture the complete action extent. In this paper, we reconceptualize actions not as isolated events, but as multi-stage action evolutions. To alleviate temporal ambiguity, we propose a novel framework driven by Linguistic Temporal Knowledge Transfer (LTKT) Network, comprising three cohesive modules. First, Temporal Stage Grounding (TSG) leverages explicitly deconstructed LLM descriptions as cross-modal priors, guiding unconstrained visual snippets to anchor their specific evolutionary stages. Second, Temporal Transition Gating (TTG) enforces forward causality on the timeline. By mandating that an execution stage is flanked by historical preparation and future completion, TTG strictly filters static noise to yield refined temporal action scores. Finally, Linguistic Knowledge Transfer (LKT) integrates knowledge distillation and contrastive learning to embed this structural knowledge into the visual backbone. Extensive experiments on THUMOS14, ActivityNet1.2 and ActivityNet1.3 demonstrate that our method achieves state-of-the-art performance.