Although large language models (LLMs) are capable of solving tasks that involve general reasoning patterns and factual knowledge, the reliability of their outputs remains highly dependent on the input text they receive. Increasing the length of the input introduces additional difficulty, as relevant facts may be distributed across different turns of a dialogue, making their integration more challenging and potentially reducing overall performance. This work introduces a method for constructing a dataset intended to assess semantic–episodic robustness in conversational settings. Central to the approach is the notion of a semantic–episodic task, defined as an evaluation unit in which a correct answer can only be produced by jointly leveraging episodic information provided exclusively within the dialogue and semantic knowledge that is not explicitly stated in the text but instead resides within the model itself. To support this, we develop a multi-stage pipeline for synthetic data generation that incorporates automated validation, enforces strict typing of outputs, and applies a graded difficulty scheme reflecting both the growth of input sequences and the increasing structural complexity of dialogues. The resulting benchmark is scalable, programmatically verifiable, and suitable for systematic comparison of models, prompting approaches, and memory mechanisms.