We present minimally supervised methods for training and testing geographic name disambiguation (GND) systems. We train data-driven place name classifiers using toponyms already disambiguated in the training text --- by such existing cues as "Nashville, Tenn." or "Springfield, MA" --- and test the system on texts where these cues have been stripped out and on hand-tagged historical texts. We experiment on three English-language corpora of varying provenance and complexity: newsfeed from the 1990s, personal narratives from the 19th century American west, and memoirs and records of the U.S. Civil War. Disambiguation accuracy ranges from 87% for news to 69% for some historical collections.