There are some domains that are in the twilight zone between language and deductive, formal reasoning. I've been into genealogy last year. It's very often deductive "detective work": say there are four women in a census with the same name and place that are listed on a birth certificate you're investigating. Which of them is it? You may rule one out on hard evidence (census suggests she would have been 70 when the birth would have happened), one on linked evidence (this one is the right age, but it's definitively the same one who died 5 years later and we know the child's mother didn't), one on combined softer evidence (she was in a fringe denomination and at the upper end of the age range) then you're left with one, etc.
Then as you collect more evidence you find that the age listed on the first one in the census was wildly off due to a transcription error and it's actually her.
You'd think some sort of rule-based system and database might help with these sorts of things. But the historical experience of expert system is that you then often automate the easy bits at the cost of demanding even more tedious data-entry. And you can't divorce data entry and deduction from each other either, because without context, good luck reading out a rare last name in the faded ink of some priest's messy gothic handwriting.
It feels like language models should be able to help. But they can't, yet. And it fundamentally isn't because they suck at grade school math.
Even linguistics, not something I know much about but another discipline where you try to make deductions from tons and tons of soft and vague evidence - you'd think language models, able to produce fluent text in more languages than any human, might be of use there. But no, it's the same thing: it can't actually combine common sense soft reasoning and formal rule-oriented reasoning very well.