Named Entity Disambiguation is the task of disambiguating named entity mentions in unstructured text and linking them to their corresponding entries in a large knowledge base such as Freebase. Practically, each text match in a given document should be mapped to the correct entity out of the corresponding entities in the knowledge base or none of them if no correct entity is found (Empty Entry). The case of an empty entry makes the problem at hand more complex, but by solving it, one can successfully cope with missing and erroneous data as well as unknown entities. In this work we present AOL's Named Entity Resolver which was designed to handle real life scenarios including empty entries. As part of the automated news analysis platform, it processes over 500K news articles a day, entities from each article are extracted and disambiguated. According to our experiments, AOL's resolver shows much better results in disambiguating entities mapped to Wikipedia or Freebase compared to industry leading products.