 Helly selection for distribution functions

A tight sequence of probability measures on the real line has a subsequence whose cumulative distribution functions converge at every continuity point of a cumulative distribution function. Choose a diagonal subsequence converging at every rational $q$, write those limits as $f(q)$, and define $F(x)={\inf }_{q>x}f(q)$. Monotonicity gives right continuity, and tightness of probability measures gives limits zero and one at the two ends of the real line. Bracketing by rational continuity approximations proves convergence at the continuity points of $F$. The corresponding probability measure is the weak subsequential limit: approximate a bounded continuous test function by step functions on a compact interval, and use tightness to bound the tails.