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Among the abilities that a sequence processing network should possess sequence disambiguation, that is, the ability to let temporal context information influence the evolution of the network dynamics, is one of the most important. In this work we propose an instance of the Bayesian Confidence Propagation Neural Network (BCPNN) that learns sequences with probabilistic associative learning and is able to disambiguate sequences with the use of synaptic traces (low pass filtered versions of the activity). We describe first how the BCPNN achieves both sequence recall and sequence learning from temporal input. Our main result is that the BCPNN network equipped with dynamical memory in the form of synaptic traces is capable of solving the sequence disambiguation problem in a reliable way. We characterize the relationship between the sequence disambiguation capabilities of the network and its dynamical parameters. Furthermore, we show that the inclusion of an additional fast synaptic trace greatly increases the network disambiguation capabilities., QC 20200330