The method used in this paper follows the intuition that, especially in literary fiction, entities appear many times throughout the text. Because each document introduces its own fictional world, each word (meaning the set of all appearances of a token with the same string) has a dominant meaning. However, not every instance of a word can be easily detected. While some might be surrounded by verbs of communication (“sagen”, “antworten”, ...), others might only be surrounded by stop words, which are not beneficial for classification. Therefore, this work introduces two passes through the text. The first pass tries to assign the dominant meaning to a word and is assumed to produce a high recall but a mediocre precision. The purpose of the second pass is to disambiguate individual instances which have been classified as a character reference but could have multiple senses. Furthermore, while the first pass might detect “Effi” and “Briest” as references, there is no information about whether the string “Effi Briest” is a single reference or two distinct references. This is solved in the second pass, which is trained for a sequential prediction and is supposed to detect the exact span of a reference.