Understanding how protein substructures fold is a crucial building block for studying large, complex protein systems. The folding mechanism of beta-hairpins has been a controversial topic over the past two decades, and many contradictory mechanisms have been proposed from both experimental and computational studies. Early computational studies of beta-hairpins were performed because of computational limits on larger structures; now, increased computational power enables us to completely dissect and understand protein substructures on the atomic level with statistically robust methods. Here, we analyze an aggregated CLN025 folding simulation dataset with simulations from several different force fields to study the beta-hairpin formation mechanism in a force-field agnostic way. We use Markov state models (MSMs) to determine that the extended state first undergoes hydrophobic collapse, and from this collapsed structure the hairpin turn is formed, which is shown to be the rate-limiting step. These mechanistic conclusions as well as the timescales of the turn formation and hydrophobic collapse events are consistent with experimental results for the order of events in CLN025 folding, which allows us to see a highly detailed process that matches up with coarser experimental data. We are also able to monitor the order of hydrogen bond formation in atomic detail. This work highlights the possibility of designing MSMs to correspond to existing experimental data, to incorporate data from multiple independent simulations in different force fields, and to coarse-grain models such that they present interpretable descriptions of beta-hairpin folding that can be verified by experiment. Finally, this work lends insight into the larger protein folding problem by demonstrating a natural means for experiment and modern computational approaches to come together to better understand biophysical phenomena: namely by the coupling of advanced theory and modern force fields with experimental validation.