I'm wondering how I should annotate correctly when using the ner.correct recipe. We're using the pre-trained en_core_web_trf model with some known NER labels (such as PERSON, PRODUCT, ORG) while also adding a few custom entities of our own (e.g., ADDRESS).
Prodigy's guidelines suggest you should reject partial NER classifications and be strict with it. So, if my sentence was "The new iPhone X is expensive", but only "iPhone" was marked by the model as PRODUCT, I should be strict and hit reject. I'm wondering, is it also possible to simply change the marking in the UI such that it includes "X" inside and then hit accept? Would it be the same?
How about sentences that mislabel some span with a wrong entity label. For example, suppose "Siri" was mislabeled as PERSON instead of PRODUCT? Should I reject or remove the PERSON label and mark it as PRODUCT in the UI and then accept? What would be the difference?
Additionally, how should I treat sentences with more than one named entity where some are correct and others are not? Should I accept/reject or change it myself in the UI?