I'm reading paper Explosion, implosion, and moments of passage times for continuous-time Markov chains: a semimartingale approach:
Let $\mathbb X$ be the state space and $\Gamma=(\Gamma_{x y})_{x, y \in X}$ the infinitesimal generator of the continuous Markov chain. The stochastic Markovian matrix $P=(P_{x y})_{x, y \in \mathbb X}$ is defined by $$ P_{x y}=\left\{\begin{array}{ll} \frac{\Gamma_{x y}}{\gamma_{x}} & \text { if } \gamma_{x} \neq 0 \\ 0 & \text { if } \gamma_{x}=0 \end{array} \text { for } y \neq x, \text { and } P_{x x}=\left\{\begin{array}{ll} 0 & \text { if } \gamma_{x} \neq 0 \\ 1 & \text { if } \gamma_{x}=0 \end{array}\right.\right. $$
The kernel $P$ defines a discrete-time $(\mathbb X, P)$-Markov chain $\tilde{\xi}=(\tilde{\xi}_{n})_{n \in \mathbb{N}}$ termed the Markov chain embedded at the moments of jumps. Define a sequence $\sigma=(\sigma_{n})_{n \geq 1}$ of random holding times distributed, conditionally on $\tilde{\xi}$, according to an exponential law. More precisely, consider $$\mathbb{P}\left(\sigma_{n} \in \mathrm{d} s | \tilde{\xi}\right)=\gamma_{\tilde{\xi}_{n-1}} \exp \left(-s \gamma_{\tilde{\xi}_{n-1}}\right) \mathbf{1}_{\mathbb{R}_{+}}(s) \,\mathrm{d} s$$ so that $\mathbb{E}\left(\sigma_{n} | \tilde{\xi}\right)=1 / \gamma_{\tilde{\xi}_{n-1}}$.
The sequence $J=(J_{n})_{n \in \mathbb{N}}$ of random jump times is defined accordingly by $J_{0}=0$ and for $n \geq 1$ by $J_{n}=\sum_{k=1}^{n} \sigma_{k}$. The life time is denoted $\zeta=\lim _{n \rightarrow \infty} J_{n}$. To have a unified description of both explosive and non-explosive processes, we can extend the state space into $\hat{\mathbb X}=\mathbb X \cup\{\partial\}$ by adjoining a special absorbing state $\partial$. The continuous-time Markov chain is then the càdlàg process $\xi=(\xi_{t})_{t \in[0, \infty]}$ defined by $$ \xi_{0}=\tilde{\xi}_{0} \text { and } \xi_{t}=\left\{\begin{array}{ll} \sum_{n \in \mathbb{N}} \tilde{\xi}_{n} \mathbf{1}_{[J_{n}, J_{n+1})}(t) & \text { for } 0<t<\zeta \\ \partial & \text { for } t \geq \zeta \end{array}\right. $$
Let $(X_t)_{t \in[0, \infty]}$ be the Markov chain defined by $(\mathbb X, \Gamma)$.
In case $\zeta < \infty$, it seems to me that we don't know how the $X_t$ behaves when $t \ge \zeta$, so the authors introduce $\partial$. This goes against my understanding because we are given $(\mathbb X, \Gamma)$ and thus we know $(X_t)_{t \in[0, \infty]}$.
Could you pleas elaborate on my confusion?