hi @yllwpr!
Here's a few links and posts that may help.
First, have you seen the Prodigy documentation on Combining Named Entity Recognition with relation extraction? This may answer a lot of your questions.
It also includes a trick that you can use Prodigy datasets as source inputs (instead of files) if you prefix it with dataset: like dataset:ner_rels_ent. Also, you can also use the suffix like reject, accept, or ignore too to the dataset in case you want to review only a subset of that Prodigy dataset. I would except you would only want to use the accept records.
The documentation on rel.manual can show how you can do both ner and rel jointly. Related, here's a post on how to review both (e.g., if you need to have an adjudicator between different annotators):
As the first link does it, it may be best to save your relationship data into a new a dataset. Also since you're considering multiple trainable components, it may be worth learning/using spacy custom config (config.cfg) files. You can pass a config.cfg file as an argument to your prodigy train function (or alternatively just use spacy train, which is really what prodigy train does). Here's a related post:
That post has a link to several open source projects available on our explosion/projects repo, like on ner and rel. I don't think one does both but perhaps can help you on how to think about structuring these projects.
Thanks again for your questions and let us know if you have any further ones!