When learning from partially labelled data (i.e., given as a subset of possible labels containing the true one), an issue that naturally arise is to determine which data should be queried to improve the accuracy of predictions, or in other word to determine an order between the partial labels to be queried. An answer to that question is to query the data that would induce the highest number of ambiguous predictions. In the K-nn case, studied in this paper, this question is similar to determining possible and necessary winners in plurality voting. In this paper, we discuss this connection as well as its use in partial label querying.