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Yay I'm glad the absurd humor resonated! That kind of goofiness makes me giggle, and the giggles are what keep me building  :D. I'm  not sure how much you want to know about the word association stuff, but here's an attempt to answer! Sorry for the wall of text.

I think you're asking about how the game knows which answers are partially correct (checkmark vs cross)?  That is largely based on the Google NGram dataset . The 2-gram data lists the frequency of certain 2-word pairs [as found in books], which can be used to figure out which word pairs have relationships or special meaning, relative to other words. Around 2020, I wrote code to slurp that up and make it searchable via my Dillfrog Context Search website (example) . So for the jam, I used my internal  API to search that data and  write code that "bakes" level files from my manually-generated list of correct answers and clues.

To generate the puzzles (i.e. the "inputs" to the baking process), I manually read through a list of "ambiguous" words that had multiple meanings , in the hopes that  they led to more interesting contexts and puzzles. For each word, I  searched Wiktionary (primarily) and my Dillfrog  Context data (secondarily) to see what words might be recognizable clues.  Then I created a TSV file that included the intended correct answers, and the clues to use.

Then, to build the  JSON "output" that the game will actually read and use, I run that baking process. The baker  slurps the TSV. For each clue, it uses that Google NGram data to spit out the  top ~500 words that occur before or after each clue (based on a PMI score if I  remember correctly...), so it can acknowledge the partially-correct guesses [for the top ~500 results].  It also adds correct answers that I missed  (e.g. plurals, if I listed the answer in singular form), based on the top ~500 connections, and does some other cleanup.

Hope that helps explain it!