Understanding the complex temporal and spatial correlations of ions in disordered perovskite oxides is critical to rationalize their functional properties. Here, we provide insights into the longstanding controversy regarding the off-centering of transition metal (TM) ions in the archetypal ferroelectric alloy KTa 1−x Nb x O 3 (KTN). By mapping the full energy ( E ) and wavevector ( Q ) dependence of the dynamical structure factor S(Q,E) using neutron scattering, and rationalizing our observations with atomistic simulations leveraging machine learning, we fully resolve the static v s dynamic nature of diffuse scattering sheets, as well as their composition ( x ) and temperature dependence. Our first-principles simulations, extended with machine-learning molecular dynamics, reproduce both inelastic neutron spectra and diffuse features, and establish how dynamically correlated TM off-centerings couple to phonons, unifying local and collective viewpoints. This study sheds light into an exemplary ferroelectric system and shows the importance of mapping the full S(Q,E) to reveal critical spatiotemporal correlations of atomic disorder from which functional properties emerge.