While the resolution of term ambiguity is important for information extraction (IE) systems, the cost of resolving each instance of an entity can be prohibitively expensive on large datasets. To combat this, this work looks at ambiguity detection at the term, rather than the instance, level. By making a judgment about the general ambiguity of a term, a system is able to handle ambiguous and unambiguous cases differently, improving throughput and quality. To address the term ambiguity detection problem, we employ a model that combines data from language models, ontologies, and topic modeling. Results over a dataset of entities from four product domains show that the proposed approach achieves significantly above baseline F-measure of 0.96. © 2013 Association for Computational Linguistics.