Named entity recognition (NER) seeks to locate and classify named entities into predefined categories (persons, organizations, brandnames, sports teams, etc.). NER is often considered as one of the main modules designed to structure a text. In this article, we describe our symbolic system which is characterized by 1) the use of limited resources, and 2) the embedding of results from other modules such as coreference resolution and relation extraction. The system is based on the output of a dependency parser that adopts an iterative execution flow that embeds results from other analysis blocks. At each iteration, candidate categories are generated and are all considered in subsequent iterations. The advantage of such a system is to select the best candidate only at the end of the process in order to take into account all the elements provided by the different modules. The system is compared to academic and industrial systems.