Process mining typically explains an event log with a single model, clustering, or classifier fitted to all cases, although real logs often mix cases that follow different sub-processes. Trace clustering separates sub-populations, but it fixes one notion of trace similarity for the whole analysis. We propose TracePrism, which treats process discovery as iterative partitioning with three characteristics. The trace encoding is adaptive: it is chosen for each working set of remaining traces from a library of encodings, with suggestions scored for that set. The partitioning is iterative: the analyst forms a candidate group based on a rule, a dominant topic, a cluster over topic weights, or a region of an embedding, accepts it if it is coherent in both process structure and outcome, and continues with the remaining traces. Decisions are revisable and recorded: a group can be reopened under a different working set or encoding, and the findings repository keeps both the earlier decision and its revision. We implement TracePrism as an open-source prototype and demonstrate it in usage scenarios on three real-world event logs. The scenarios also revealed behavior that we had not anticipated, such as process drift.

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