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This paper presents a set of algorithms for distinguishing personal names with multiple real referents in text, based on little or no supervision. The approach utilizes an unsupervised clustering technique over a rich feature space of biographic facts, which are automatically extracted via a language-independent bootstrapping process. entities are then partitioned and linked to their real referents via the automatically extracted biographic data. Performance is evaluated based on both a test set of hand-labeled multi-referent personal names and via automatically generated pseudonames.