Many NATO navies are in the process of replacing their dedicated minehunting vessels with systems of heterogeneous, unmanned modules. While traditional ship-based assets prosecute sonar contacts in sequence through to neutralisation, modern systems employ unmanned vehicles equipped with side-looking sonar to detect and classify minelike contacts in a full area segment before proceeding with contact identification and mine neutralisation. This shift in technology and procedure brings important operational advantages, but also introduces a need to modify the traditional minehunting performance evaluation based on the percentage clearance metric. Previous works have demonstrated that the achieved detection and classification performance of modern minehunting systems can be estimated from the collected sonar data (through-the-sensor) and reported as detailed geographical maps. This paper extends the map-based evaluation approach to the identification and neutralisation phases, and also includes the case where some of the contacts or mines intentionally are left unprosecuted, e.g. disposal of only the specific mines required for establishing a safe sailing route. Each map cell is assumed to be sufficiently small to contain at most one sonar contact and can thus be assigned a status based on the hunting results for that cell: minelike contact, identified mine, etc. To this end we derive Bayesian formulations of a new performance metric: the probability of a remaining mine in a given cell. Furthermore, we show that this metric provides consistent multi-phase performance evaluation and estimates of the mine impact risk for a follow-on ship transiting a specified route.