We present and evaluate new methods for incremental entity resolution as needed for the completion of knowledge graphs integrating data from multiple sources. Compared to previous approaches we aim at reducing the dependency on the order in which new sources and entities are added. For this purpose, we consider sets of new entities for an optimized assignment of them to entity clusters. We also propose the use of a light-weight approach to repair entity clusters in order to correct wrong clusters. The new approaches are integrated within the FAMER framework for parallel and scalable entity clustering. A detailed evaluation of the new approaches for real-world workloads shows their high effectiveness. In particular, the repair approach outperforms other incremental approaches and achieves the same quality than with batch-like entity resolution showing that its results are independent from the order in which new entities are added.