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In this paper we report the participation of ADEL to the OKE 2017 challenge. In particular, an adaptive entity recognition and linking framework that combines various extraction methods for improving the recognition level and implements an efficient knowledge base indexing process to increase the performance of the linking step. We detail how we deal with fine-grained entity types, either generic (e.g. Activity, Competition, Animal for Task 2) or domain specific (e.g. MusicArtist, SignalGroup, MusicalWork for Task 3). We also show how ADEL can flexibly link entities from different knowledge bases (DBpedia and MusicBrainz). We obtain promising results on the OKE 2017 challenge test dataset for the first three tasks.