Thanks! I actually planned to learn some prompt engineering best practice and kept iterating, but the performance kept getting worse and I reverted to the first

Thanks! I actually planned to learn some prompt engineering best practice and kept iterating, but the performance kept getting worse and I reverted to the first version. I ended up using a pretty straightforward one just asking to find events with dates, using the precision available for example, if only year is available just using year, otherwise it will make something up like YYYY/1/1, output the event in JSON format with a headline and a more detailed description. My experience tells me a better model is much more important than the prompt engineering (but ofc the prompt needs to be basically right). For cost consideration, I was trying to use openai 4o-mini and the performance was bad no matter how hard I tried for better prompt, but when I switch to 4o, it works for even a basic prompt. One issue it's struggling is that I want it to output a negative year if it's BC/BCE, it sometimes messes it up, either using positive year for BC/BCE or negative year even it's AD. I changed the prompt to include date in the detailed description because I found most of the time the date in the description is actually right, and ask it correct the date by referring what's in the description to be positive or negative. I feel this fixed the issue for the most time, but seems still happens for some case. Overall, the challenge here is that I don't really have a benchmark to evaluate the quality of the timelines yet, I basically do a manual random sampling of timelines to check the quality myself. The future work (if I keep my current interest on this) would be building a benchmark which can auto evaluate the quality so that the iteration of prompt can happen and also might try different LLMs.