Entity linking (EL) is the task of linking a textual named entity mention to a knowledge base entry. Traditional approaches have addressed the problem by dividing the task into separate stages: entity recognition/classification, entity filtering, and entity mapping, in which different constraints are used to improve the system’s performance. Nevertheless, these constraints are executed separately and cannot be used interactively. In this paper, we propose an integrated solution to the task based on a Markov logic network (MLN). We show how the stage decision can be formulated and combined in an MLN. We conducted experiments on the biomedical EL task, gene mention linking (GML), and compared our model’s performance with those of two other GML approaches. Our experimental results provide the first comprehensive GML evaluations from three different perspectives: article-wide precision/recall/F-measure (PRF), instance-based PRF, and question answering accuracy. This paper also provides formal definitions of all of the above EL tasks. Experimental results show that our method outperforms the baseline and state-of-the-art systems under all three evaluation schemes.