In spatio-temporal reasoning, granularity is one of the factors to be considered when aiming at an effective and efficient representation of space and time. There is a large body of work which addresses the issue of granularity by representing space and time on a qualitative level. Other approaches use a predefined scale which implicitly determines granularity (e.g., seconds, minutes, hours, days, month, etc.). However, there are situations where the right level of granularity is unknown in the beginning, and is only determined in the problem solving process itself. This is the case in machine learning, where the learner has to find a representation for a problem and with that the right granularity for representing space and time. This paper introduces an algorithm which determines the most appropriate level of granularity during training. It uses several description logic learners as the learners, and the positive and negative examples presented to them as the determinators for refining coarse temporal representations to the most appropriate level of granularity.