This work examines the relevance of uncertain temporal information. A key observation that motivates the analysis presented here is that in the presence of uncertainty, relevance of information degenerates as time evolves. This paper presents an empirical quantitative study of the degeneration of relevance in time-sliced Belief Networks that aims at extending known results. A simple technique for estimating an upper bound on the relevance time is presented. To validate the proposed technique, results of experiments using realistic and synthetic time-sliced belief networks are presented. The results show that the proposed upper bound holds in more than 98% of the experiments. These results have been obtained using a modified version of the dynamic belief networks roll-up algorithm.