When interacting with a system, users often request information about an entity by specifying a string that may have multiple possible interpretations. Humans are quite good at recognizing when an ambiguity exists and resolving the ambiguity given contextual cues. This disambiguation task is more complicated in automated systems. As systems have more and more different entities and entity types available to them, they may better detect potential ambiguities (e.g., 'orange' as a color or fruit). However, it becomes harder to resolve entities automatically and effectively. In this position paper we discuss challenges in interacting with users to ask clarifying questions for entity identification. We propose three approaches, illustrating their strengths and weaknesses.