Abstract One of the key steps in language resource creation is the identification of the text segments to be annotated, or markables– in our case, the (potentially nested) noun phrases in coreference resolution (or mentions). In this paper, we present a method for identifying markables for coreference annotation that combines high-performance automatic markable detectors with checking with a GameWith-A-Purpose (GWAP) and aggregation using a Bayesian annotation model. The method was evaluated both on news data and data from a variety of other genres and results in an improvement on F1 of mention boundaries of over seven percentage points when compared with a state-of-the-art, domain-independent automatic mention detector, and almost three points over an in-domain mention detector. One of the key contributions of our proposal is its applicability to the case in which markables are nested, as is the case with coreference markables; but the GWAP and several of the proposed markable detectors are task- and language-independent and are thus applicable to a variety of other annotation scenarios.