ODIN, the Online Database of INterlinear text, is a resource built over language data harvested from linguistic documents (Lewis, 2006). It currently holds approximately 190,000 instances of Interlinear Glossed Text (IGT) from over 1100 languages, automatically extracted from nearly 3000 documents crawled from theWeb. A crucial step in building ODIN is identifying the languages of extracted IGT, a challenging task due to the large number of languages and the lack of training data. We demonstrate that a coreference approach to the language ID task significantly outperforms existing algorithms as it provides an elegant solution to the unseen language problem. We also discuss several issues that make automated Language ID and the maintenance of ODIN very difficult.