4 (source code)
= 4
{scope}
Throughout the survival questions, use the paper's notation $F(t)=\mathbb P(T>t)$ for the <survivor function>, not for a cumulative distribution function. To construct the <Kaplan–Meier estimator>, sort the distinct observed event times $a_j$. Let $r_j$ be the size of the <risk set> immediately before $a_j$, and $d_j$ the number of events at that time. The estimated conditional chance of surviving that event time is $1-d_j/r_j$. Multiplying these conditional probabilities gives
$$
\boxed{\widehat F(t)=\prod_{a_j\le t}\left(1-\frac{d_j}{r_j}\right).}
$$
A <right censoring> time reduces later <risk sets> but causes no jump itself. The usual interpretation as a population <survivor function> uses <independent censoring>.
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