In this paper, we present a new approach for recognition and grounding of geographic proper names for German. Named Entity Recognition (NER) in German is more difficult than in English because not only proper names, but all nouns start with capital letters, which results in a large pool of potential ambiguous entities. Our approach makes critical use of a geographic knowledge base that is more detailed (down to the level of streets) and more structured than most knowledge bases used before. We have designed a three-step model (spotting, typing, referencing) that specifies the sources of information that are necessary for geo-tagging and their dependency relationships. Basic aspects of the model were implemented and evaluated in a proof of concept. The model can be applied to other NER tasks by simply substituting the appropriate knowledge base for the one used here and retraining the model.