Coreference Resolution is a challenge which has seen the interest of researchers for half a century, but at the current point in time, even state-of-the-art algorithms (Joshi et al., 2019)cannot produce reliable results when applied in a fully automatic manner. The main problem of an automatic coreference resolution system is that decisions that occur late in the text can heavily influence prior decisions and so the resulting clustering and its composition is hard to understand. We found that, when applying the end-to-end coreference resolution algorithm (Lee et al., 2017), the algorithm tends to reset its understanding of the text every couple of paragraphs, which results in a mixture of grave errors when aggregated over the document. Coreference resolution is a necessary and very important step however, when analysing the content of a literary text. Different engines detect knowledge on a local level and coreference resolution aggregates this knowledge to the document scope of the text. This means that, without a reliable coreference resolution module, most applications that require an aggregated view of the texts (which is very important for most distant reading experiments) cannot be researched efficiently. In this work, we try to overcome the challenge of coreference resolution by presenting a semi-automatic mechanism instead of a fully automatic system. This mechanism allows the users to integrate their prior knowledge about characters of a literary text and their relations. We do this by parsing this knowledge into a machine-readable data structure and integrate this knowledge into our rule-based coreference resolution system, which was extended from (Krug et al., 2015).