Our paper provides the following contributions: (1) an approach that jointly reasons about both within-doc entities and their alignment to KBentities by dynamically adjusting a ranked list of candidate alignments, during coreference, (2) Utilization of a larger set of surface string variations for each entity candidate by using links that appear all over the web (Spitkovsky and Chang, 2012) , (3) A combination of these approaches that improves upon a competitive baseline without a knowledge base by 1.09 B 3 F1 points on the ACE 2004 data, and outperforms the state-of-the-art coreference system (Stoyanov and Eisner, 2012) by 0.41 B 3 F1 points, and (4) Accurate predictions on documents that are difficult for coreference, such as the transcript documents that were omitted from the evaluation in <cite>Ratinov and Roth (2012)</cite> , and documents that contain a large number of mentions.