Crowdsourcing services make it possible to collect huge amount of annotations from less trained crowd workers in an inexpensive and efficient manner. However, unlike making binary or pairwise judgements, labeling complex structures such as ranked lists by crowd workers is subject to large variance and low efficiency, mainly due to the huge labeling space and the annotators' non-expert nature. Yet ranked lists offer the most informative knowledge for training and testing in various data mining and information retrieval tasks such as learning to rank. In this paper, we propose a novel generative model called 'Thurstonian Pairwise Preference' (TPP) to infer the true ranked list out of a collection of crowdsourced pairwise annotations. The key challenges that TPP addresses are to resolve the inevitable incompleteness and inconsistency of judgements, as well as to model variable query difficulty and different labeling quality resulting from workers' domain expertise and truthfulness. Experimental results on both synthetic and real-world datasets demonstrate that TPP can effectively bind pairwise preferences of the crowd into rankings and substantially outperforms previously published methods.