The task of zero-shot relation extraction focuses on the extraction of relations not seen during training time. Commonly, additional information about the relation such as the relation name or a description of the relation is utilised. In this work, we analyze whether a relation extractor can benefit from the inclusion of fine-grained type information about the involved entities. This is based on the intuition that relation descriptions might contain ontological information on the domain and range of the entity types that are usually put into relation. For that, we follow a cross-encoding setup where we encode both, the entity information and relation information, as one sequence and learn to score the representation. We examine this method on several datasets and show that the inclusion of the fine-grained type information leads to an improvement in performance.