With user-specified knowledge at hand, we examined whether it just serves as an addition to our rule-based algorithm, or if it is able to replace the parts of our algorithm, which handle names and non-pronominal noun phrases (the algorithm cannot be replaced completely since user-specified knowledge does not help with pronouns). To assess this theory, we created the following algorithm: In the first step, all mentions which are identified to belong to the same character from user-specified knowledge (how this is done is described in the previous section) are merged into the same cluster. After that, only the parts of our algorithm which handle pronouns are applied. Table 3 shows the results of this string-matching baseline algorithm and the difference to the rule-based algorithm using the same user-specified knowledge. While the precision of the baseline is higher in all cases, its recall is lower, often by a rather large margin. This leads to the MUC score being slightly better in three cases but being worse in all other cases. The difference is even more noticable when looking at the B-Cubed scores: With two exceptions, they are always more than 5% worse than the results of the rule-based algorithm with user-specified knowledge. To summarize, one can say that the algorithm cannot be replaced by string-matching without a significant loss of quality.