Understanding the fundamental mechanisms governing the production of meaning in natural language processing is critical for designing safe and engaging human-agent interactions. Research in cognitive science and social psychology has demonstrated that human semantic processing exhibits contextuality more consistent with quantum logical mechanisms than classical Boolean theories. Recent studies have found similar quantum-like behavioral signatures in large language models, including clear violations of the Bell inequality during interpretation of ambiguous expressions. This work explores the CHSH parameter across the inference parameter space of language models spanning four orders of magnitude in scale, cross-referencing findings with MMLU benchmarks, hallucination rates, and nonsense detection metrics to understand semantic contextuality in neural language systems.