Throughout this question, for an arbitrary real valued continuous time stochastic process $Y$, I define $\{\mathcal{F}_t^Y\}_{t \ge 0}$ to be the filtration generated by $Y$. I also define $\text{sgn}(x) = \mathbf{1}(x > 0) - \mathbf{1}(x \leq 0)$
The standard argument that Tanaka's SDE has no strong solutions for a particular probability space is as follows, but I do not understand the last part of it: it suffices to consider a probability space $(\Omega, \mathcal{F}, P)$ on which there exists a Brownian motion $B$, and we define our filtration $\{\mathcal{F}_t\}_{t \ge 0}$ to be that which is generated by $B$. If $$dX_t = \text{sgn}(X_t) dB_t $$ has a strong solution, then $$B_t = \int_0^t \text{sgn}(X_s)dX_s = |X_t| - L_t^0$$ where $L_t^0$ is the local time of $X$ about $0$ at time $t$. It is well known that $$L_t^0 = |X_t| - \lim_{h \rightarrow 0} \frac{1}{h}\text{Leb}\left( \{s \in [0,t] : \left|X_s \right| \leq h \right)$$ and thus $$\mathcal{F}_t^{X} \subseteq \mathcal{F}_t^B \subseteq \mathcal{F}_t^{|X|}$$ which is a contradiction, since $$\text{sgn}(X_t) \text{ cannot possibly be } \mathcal{F}_t^{|X|} \text{ measurable.} \qquad \qquad \textbf{(1)}$$ Intuitively, $\textbf{(1)}$ is true, but how does one rigorously go about showing this?
I try to do so by contradiction, but it leads me nowhere after a bit. If $\text{sgn}(X_t)$ were $\mathcal{F}_t^{|X|}$ measurable, then there would exist some functional $f$ defined on the space of continuous functions on $[0,t]$ such that $$\text{sgn}(X_t) = f(|X_s|, s \leq t) $$ I know this is intuitively obvious to be a contradiction, but I cannot formally show it. Any help would be appreciated!