Searching for people on the Web is one of the most common query types submitted to Web search engines today. However, when a person name is queried, the returned Webpages often contain documents related to several distinct namesakes who have the queried name. The task of disambiguating and finding the Webpages related to the specific person of interest is left to the user. Many Web People Search (WePS) approaches have been developed recently that attempt to automate this disambiguation process. Nevertheless, the disambiguation quality of these techniques leaves major room for improvement. In this article, we present a new WePS approach. It is based on issuing additional auxiliary queries to the Web to gain additional knowledge about the Webpages that need to be disambiguated. Thus, the approach uses the Web as an external data source by issuing queries to collect co-occurrence statistics. These statistics are used to assess the overlap of the contextual entities extracted from the Webpages. The article also proposes a methodology to make this Web querying technique efficient. Further, the article proposes an approach that is capable of combining various types of disambiguating information, including other common types of similarities, by applying a correlation clustering approach with after-clustering of singleton clusters. These properties allow the framework to get an advantage in terms of result quality over other state-of-the-art WePS techniques.