Social simulation studies are complex, because they typically combine various sources of data and hypotheses, that are integrated by intertwined processes, of model building, simulation experiment execution, and analysis. Various documentation approaches exist that support transparency and traceability of social simulation studies. The exploitation of provenance standards allows for making the information about what sources and activities contributed to the generation of an entity, e.g., simulation model, queryable and computationally accessible. Therefore, provenance patterns have been defined to capture central activities and entities. Activities include model building, calibration, analysis, and validation. Entities are simulation model, simulation experiment (its specification), and research question. Here we refine and extend this approach to address specific challenges of social agent-based simulation studies, i.e., activities such as collecting and analyzing primary data about human decisions, or collecting and assessing the quality of secondary data. This allows us to tell the whole story of these simulation studies in a comprehensive manner. We illustrate the potential of the approach by applying it to central activities and results of the Bayesian Agent-Based Population Studies project and implementing it in a web-based provenance tool.