KEA is a named entity annotation system based on a fine-granular context model taking into account heterogeneous text sources as well as text created by automated multimedia analysis. The source texts can have different levels of accuracy, completeness, granularity and reliability which influence the determination of the current context. Ambiguity is solved by selecting entity candidates with the highest level of probability according to the predetermined context. The new implementation begins with the detection of groups of consecutive words (n-gram analysis) and a lookup of all potential DBpedia candidate entities for each n-gram. The disambiguation of candidate entities is based on a scoring cascade. KEA is available as NIF-based web- service.