Large language models (LLMs) frequently prioritize parametric world knowledge over provided context -- a failure mode that is particularly catastrophic in enterprise or counterfactual settings where local facts contradict web-scale training data. While modern reasoning models improve general response quality, they fail to resolve these underlying prior knowledge biases even when generating a high volume of costly thinking tokens. We propose a context-editing framework that addresses this by performing selective abstraction over entities that appear in both the context and the question. Our approach replaces these overlapping entities with typed, indexed identifiers (e.g., Paris with City łangle1\rangle), suppressing spurious lexical associations while preserving the relational structure required for grounded reasoning. This mechanism is decoupled into an offline preprocessing stage and a lightweight inference-time substitution, requiring no model retraining. Experiments across counterfactual and multi-hop knowledge-conflict benchmarks demonstrate consistent accuracy gains across many model families, open- and closed-sourced, especially for small to medium sized LLMs (0.5B to 18B). Notably, our framework yields up to a 25% improvement over state-of-the-art instruct models and 13% over reasoning models, establishing symbolic abstraction as a highly cost-efficient solution for ensuring context fidelity in LLMs.