This categorisation and distribution analysis provides valuable direction for prioritising footnote types in such automated processes. Moreover, the analysis of the most used references gave a good indicator of what material to feed the large language models with to generate historically accurate content. Lastly, the high percentage of miscellaneous footnotes hints at the fact that these might be the type of footnotes where additional AI support could speed up the editing process.