Knowledge bases are an important resource for question answering systems and search engines but often suffer from incompleteness. This work considers the problem of knowledge base completion (KBC). In the context of natural language processing, knowledge bases comprise facts that can be formalized as triples of the form (entity 1, relation, entity 2). A common approach for the KBC problem is to learn representations for entities and relations that allow for generalizing existing connections in the knowledge base to predict the correctness of a triple that is not in the knowledge base. In this work, I propose the context path model, which is based on this approach. In contrast to existing KBC models, it also provides explanations for predictions. For this purpose, it uses paths that capture the context of a given triple. The context path model can be applied on top of several existing KBC models. In a manual evaluation, I observe that most of the paths the model uses as explanation are meaningful and provide evidence for assessing the correctness of triples. I also show in an experiment that the performance of the context path model on a standard KBC task is close to a state of the art model.