Some of the main ranking features of today's search engines reflect result popularity and are based on ranking models, such as PageRank, implicit feedback aggregation, and more. While such features yield satisfactory results for a wide range of queries, they aggravate the problem of search for ambiguous entities: Searching for a person yields satisfactory results only if the person we are looking for is represented by a high-ranked Web page and all required information are contained in this page. Otherwise, the user has to either reformulate/refine the query or manually inspect low-ranked results to find the person in question. A possible approach to solve this problem is to cluster the results, so that each cluster represents one of the persons occurring in the answer set. However clustering search results has proven to be a difficult endeavor by itself, where the clusters are typically of moderate quality. A wealth of useful information about persons occurs in Web 2.0 platforms, such as LinkedIn, Wikipedia, Facebook, etc. Being human-generated, the information on these platforms is clean, focused, and already disambiguated. We show that when searching for ambiguous person names the information from such platforms can be bootstrapped to group the results according to the individuals occurring in them. We have evaluated our methods on a hand-labeled dataset of around 5,000 Web pages retrieved from Google queries on 50 ambiguous person names.