The lifted dynamic junction tree algorithm (LDJT) efficiently answers filtering and prediction queries for probabilistic relational temporal models by building and then reusing a firstorder cluster representation of a knowledge base for multiple queries and time steps. Specifically, this paper contributes (i) a relational forward backward algorithm with LDJT,(ii) smoothing for hindsight queries, and (iii) different approaches to instantiate a first-order cluster representation during a backward pass. Further, our relational forward backward algorithm makes hindsight queries with huge lags feasible. LDJT answers multiple temporal queries faster than the static lifted junction tree algorithm on an unrolled model, which performs smoothing during message passing.