The fat cat is sitting on a mat.

And even within the world of the sentence, it only has a vague meaning. What exactly makes the cat fat? Is it neutered or lazy or overfed? Why is it sitting on a mat - is the mat outside a door, is it waiting to be let in? What kind of a cat is it - could it be a big, dangerous cat? The sentence is laden with signifiers and unknowns. Western children are taught to consider sentences like these in the abstract, but it's not a natural way of thinking, because it's not practical in a life lived connected to the world.

Abstract hypotheticals are the hallmark of more disconnected concerns, and we teach our children this early, in part using silly, deliberately vague sentences like these, and discouraging curious questions that might resolve the ambiguities.

An AI system that's designed to handle abstract sentences like these is not one designed to understand human language, because humans don't reason like this unless they're thinking analytically - and even then, they do so blinkered with biases and errors.

or someone who did not grow up using the English language.

Or, you know, you can use a cut-off point, like 18 or so.

Predicate calculus is an entirely parallel system that only has a representation in a strict subset of human language. A reverse mapping is hopeless and misguided.

I feel like this line of thought, that you can box up words with really concrete meanings that you can then reason about with logic systems is a kind of trap for people who've spent too much time in an analytic frame of the world. It's one where the smarter you are, the further down the road you can get without realising it's a dead end. At best, such a system could only augment.

It will totally be fine for 99% of applications. Marginal returns.