The MultiCoNER II shared task aims at detecting complex, ambiguous named entities with fine-grained types in a low context setting. Previous winning systems incorporated external knowledge bases to retrieve helpful contexts. In our submission we additionally propose splitting the NER task into two stages, a Span Extraction Step, and an Entity Classification step. Our results show that the former does not suffer from the low context setting comparably, and in so leading to a higher overall performance for an external KB-assisted system. We achieve 3rd place on the multilingual track and an average of 6th place overall.