In an instant search setting such as Netflix Search where results are returned in response to every keystroke, determining how a partial query maps onto broad classes of relevant entities orfacets --- such as videos, talent, and genres --- can facilitate a better understanding of the underlying objective of that query. Such a query-to-facet mapping system has a multitude of applications. It can help improve the quality of search results, drive meaningful result organization, and can be leveraged to establish trust by being transparent with Netflix members when they search for an entity that is not available on the service. By anticipating the relevant facets with each keystroke entry, the system can also better guide the experience within a search session. When aggregated across queries, the facets can reveal interesting patterns of member interest. A key challenge for building such a system is to judiciously balance lexical similarity with behavioral relevance. In this paper, we present a high level overview of a Query Facet Mapping system that we have developed at Netflix, describe its main components, provide evaluation results with real-world data, and outline several potential applications.