We present a joint system for named entity recognition (NER) and entity linking (EL), allowing for named entities mentions ex- tracted from textual data to be matched to uniquely identifiable entities. Our approach relies on combined N E R modules which transfer the disambiguation step to the EL component, where referential knowledge about entities can be used to select a correct entity reading. Hybridation is a main fea- ture of our system, as we have performed experiments combining two types of NER, based respectively on symbolic and statis- tical techniques. Furthermore, the statisti- cal EL module relies on entity knowledge acquired over a large news corpus using a simple rule-base disambiguation tool. An implementation of our system is described, along with experiments and evaluation re- sults on French news wires. Linking ac- curacy reaches up to 87%, and the NER f- measure up to 83%.