Scenario-based testing is increasingly common for evaluating highly automated driving systems. The methodology is also used in the development and evaluation of highly automated rail vehicles. A scenario set can be derived using either a data-based or knowledge-based approach. As real measurement data is limited in the railway sector, our focus is on scenarios derived from knowledge-based methods. In addition to possessing a suitable knowledge base, it is crucial to follow a methodical approach to scenario definition and establish appropriate rules for creating scenarios. The objective is to achieve a convergence of scenario outcomes to enable a comprehensive evaluation of the automated system through testing. This paper is based on our scenario generation method for highly automated on-sight train operation, using an automated shunting system as an example, and highlights the challenges that arise when combining scenario elements. The paper demonstrates the logical combination process and describes the transfer of knowledge data into a scenario. It also outlines the rules and conditions for linking individual objects in the given scenario. The final section of the paper presents the methodological procedure for achieving convergence of the generated scenario set.