<p>Process mining is a data-driven technique that leverages event logs to analyze, visualize, and improve business processes. However, data quality is often low in real-world settings due to various event log imperfections, which, in turn, degrade the accuracy and reliability of process mining insights. One notable example is the elusive case imperfection pattern, describing the absence of case identifiers responsible for linking events to a specific process instance. Elusive cases are particularly problematic, as process mining techniques rely heavily on the accurate mapping of events to instances to provide meaningful and actionable insights into business processes. To address this issue, the study follows the Design Science Research paradigm to iteratively develop a method for repairing the elusive case imperfection pattern in event logs. The proposed Hybrid Elusive Case Repair Method (<i>HERE</i>) combines a traditional, rule-based approach with generative artificial intelligence, specifically the Transformer architecture. By integrating domain knowledge, <i>HERE</i> constitutes a comprehensive human-in-the-loop approach, enhancing its ability to accurately repair elusive cases in event logs. The method is evaluated by instantiating it as a software prototype, applying it to repair three publicly accessible event logs, and seeking expert feedback in a total of 21 interviews conducted at different points during the design and development phase. The results demonstrate that <i>HERE</i> makes significant progress in addressing the elusive case imperfection pattern, particularly when provided with sufficient data volume, laying the groundwork for resolving further data quality issues in process mining.</p>