Why is the sigmoid of a linear model equal to the probability of the target being $1$?

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From this resource, the writer starts with a linear model:

$$ y = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + ... + \beta_m x_m $$

and then makes the RHS sigmoidal. This must then make the LHS sigmoidal to preserve the equality.

$$\implies S(y) = S(\beta_0 + \beta_1 x_1 + \beta_2 x_2 + ... + \beta_m x_m) = \frac{1}{1+ \exp^{-(\beta_0 + \beta_1 x_1 + \beta_2 x_2 + ... +\beta_m x_m)}} $$

But he instead writes

$$ P(y=1) = S(\beta_0 + \beta_1 x_1 + \beta_2 x_2 + ... + \beta_m x_m) $$

Could someone explain the implication that $S(y) = P(y=1)$?