It's well known that even current LLMs do not perform well on logic games when you change the names / language used.

i.e. try asking it to swap the meanings of the words red and green and ask it to describe the colors in a painting and analyse it with color theory - notice how quickly the results degrade, often attributing "green" qualities to "red" since it's now calling it "green".

What this shows us is that training data (where the associations are made) plays a significant role in the level of answer an LLM can give, no matter how good your context is (at overriding the associations / training data). This demonstrates that training data is more important (for "novel" work) than context is.