The popular microblogging service Twitter provides a vast amount of short messages that contains interesting information for Information Extraction tasks. This paper presents a rule-based system for the recognition and semantic disambiguation of named entities in tweets. As our experimental results shows, performance of this approach measured through BDM looks promising when using Linked Data as Freebase and syntactical context for disambiguation.