Knowledge graphs (KGs) and their related ontologies constitute a key component in modern knowledge-based systems. However, hand-crafting these is not scalable, particularly due to the rate at which knowledge changes in many real-world applications. Partially automating the process of extracting and even modelling knowledge has therefore been a subject of research for many years. Nevertheless, accurate and reliable KG construction from natural language documents still remains a difficult task with many challenges, even in light of the impressive recent advances in language modelling. This paper focuses on one of those challenges, namely the extraction of accurate entity representations from text documents in order to facilitate relation extraction (RE). We present a novel method for generating document-contextualized input representations for entities using a masked language model (MLM) without the need for any sort of fine-tuning. These representations are then used as inputs to the same MLM that generated them, alleviating the need to include entire documents when prompting. Our results show that these representations 1) improve the ability of the MLMs BERT and RoBERTa to identify statements that represent correct relations between two entities; and 2) allow BERT to perform on par with the fine-tuned MLMs BioBERT and PubMedBERT.