We address the problem of rank elicitation as-suming that the underlying data generating pro-cess is characterized by a probability distribu-tion on the set of all rankings (total orders) of a given set of items. Instead of asking for complete rankings, however, our learner is only allowed to query pairwise preferences. Using information of that kind, the goal of the learner is to reliably predict properties of the distribution, such as the most probable top-item, the most probable rank-ing, or the distribution itself. More specifically, learning is done in an online manner, and the goal is to minimize sample complexity while guaran-teeing a certain level of confidence.