Conventional binary systems force a choice between true and false, often obscuring the inherent uncertainty in statistical models. Ternary State Algebra (TSA) introduces a first-class unknown or abstain state, represented as 0 in the set {-1, 0, +1}. This shift allows for a unified substrate where logical operations, like those found in SQL's three-valued logic, coexist with the arithmetic efficiency of balanced-ternary representations used in low-precision neural networks.