Hi! You can always load an existing dataset back in for annotation – just use the dataset: syntax instead of the source file (e.g. dataset:your_previous_dataset) and then save the results to a new dataset. You'll then see the existing annotations, and you'll be able to add new ones on top of that.
Alternatively, you can also create an entirely new dataset and only annotate the new labels. This can sometimes make sense if you're annotating one label at a time. Prodigy will merge all annotations on the same text when you train or export the data with data-to-spacy.
(In theory, you could also edit the JSON manually, if you really want to – you can find an example of the format here: Annotation interfaces · Prodigy · An annotation tool for AI, Machine Learning & NLP But it's not necessarily something I'd recommend. It does work, though – just add the spans with their start and end index and the given label.)
The JSON file is the data format used by spacy train: Command Line Interface · spaCy API Documentation
If you mean the POS tagger and its pretrained weights: Yes, you can just add that to your pipeline. See here for an example: Add custom NER model from prodigy to spacy pipeline - #2 by ines
If you mean the POS tags predicted by the model on your specific data: Yes, you can use spaCy to label the data for you automatically, using the existing model. You could also use an annotation recipe like pos.correct and manually review and correct the model's predictions on your data, and then train a POS tagger using the collected annotations.