Relational Graph Convolutional Networks (RGCNs) identify relationships within a Knowledge Graph to learn real-valued embeddings for each node and edge. Recently, researchers have proposed explanation methods to interpret the predictions of these black-box models. However, comparisons across explanation methods is difficult without a common dataset and standard evaluation metrics to evaluate the explanations. In this paper, we propose a method, including two datasets (Royalty-20k and Royalty-30k), to benchmark explanation methods on the task of explainable link prediction using Graph Neural Networks. We report the results of state-of-the-art explanation methods for RGCNs.