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I don't and I can give you a couple if you think it would help, but I tried it with 4o and o4-mini and it didn't work well. I've done hundred, if not thousands, of these manually, and I checked several that terminate at different stages of the analysis to see if any would correspond with what I determined originally. I would add the caveat that the actual algorithm would be more complex; I was writing this as I was leaving work on Friday afternoon and there were several rules that I failed to consider that came up when I ran it, most notable that if there are two conflicting months of release then use the last usual release day of the earlier month (assuming the months ore consecutive or otherwise close together or that there's no reason to believe that the earlier month is wrong). There are also a bunch of edge cases that I didn't put in, like singles that are released locally before being given a national release some months later (occasionally happened with smaller labels in the 1960s who had local hits that would get picked up nationally), and specifying which country of release to use, and a bunch of other stuff that's too uncommon to even mention. That out of the way here are the trends I found:
Miscellaneous Notes: It made a few odd errors along the way. It wasn't able to determine a typical release day for any label and always defaulted to Monday, except in the case of British releases, where it defaulted to Friday. These were the most common release days in the 60s and 70s for these territories, but they were by no means universal, and I specifically tested it with labels that released on other days. It also made some errors where it would give an incorrect date, e.g., It would say June 18th was a Monday in a particular year but it was really a Wednesday.
Conclusion: It's capable of producing reasonable estimates that are relatively close to my own estimates, but are nonetheless almost always off. If I don't have a credible release date, almost all estimates will be derived from either copyright data, trade publication review dates, or ARSA chart dates. Since the models seem incapable of accessing any of these, they are functionally useless. They're limited to finding dates I can already find more easily without AI, and estimating release dates based on chart data. I'm not familiar with o-3 or how it compares to what I was able to use, but if you think it could succeed where the others failed, let me know and I'll give you a few to try out. I don't want to waste your tokens on a vanity project for an extremely niche application, but I understand you might be interested in how these models work. Also consider that I'm an AI skeptic who would pay for a service like this if it could reliably do what I need it to do. A lot of my skepticism, though, stems from the fact that it seems incapable of accessing information that's trivial for an actual person to access.
Go for it. o3 is far more competent than either of 4o and 4o-mini. It will probably look for better sources, and spend tens of minutes at the task if it deems it necessary.
A helpful analogy is that 4o is a smooth talking undergrad with lots of charisma and some brains. o3 is an autistic grad-student, far more terse, but far more capable in return. It justifies the price of subscription for me.
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