Does textcat use NER entities as features?

Simple question, I hope.

Say I use a base model with NER to train a textcat compent, e.g.

prodigy textcat.batch-train my_dataset en_vectors_web_lg --output my_model

The resulting pipeline will be something like:


Will the textcat component use the extracted ner entities - e.g. PERSON, NORP, etc. - as features in any way? Or will it just use the text/syntax/vectors the same as if the ner wasn't even there?

By default, spaCy's pipeline components are independent and don't share any features between them (also see this docs section on the pipeline component order). So whether the entity recognizer is there or not won't make a difference.

What if I really want spaCy to use NER to improve text categorization?

E.g. what if I want these two sentences to be categorized differently? Obviously the NER recognizing GPE would be helpful:
"We live in Denmark" vs. "We live in fear".

How can I get TextCat to take NER into consideration?