Natural language understanding entails the ability to comprehend the relations between various people, objects or events throughout one, or multiple, text(s). Event coreference resolution (ECR) is a discourse-based natural language processing (NLP) task which aims to link those textual events, be they real or fictional, that refer to the same conceptual event. In this paper, we introduce a novel end-to-end approach for cross-document ECR which combines expert-level positional knowledge and graph-based representations in order to create a memory-efficient and accurate system meant for the detection and resolution of events in large document collections. We make three fundamental architectural changes to a current state-of-the-art cross-document ECR system and show that our approach outperforms this earlier model (+ 4% CONLL F1) on a large Dutch ECR dataset. Moreover, we show through in-depth qualitative and quantitative analysis that our proposed approach consistently detects more relevant events and suffers notably less from the typical issues models exhibit when predicting coreference chains.