Understanding the generality of the NER problem

Named-entity recognition (NER) is a well-known problem in the NLP literature.

It typically addresses the problem to locate and classify named entities in text, e.g. Organizations and Products.

I'm trying to solve a similar problem but, in my view, a bit more general. Given an input text, I want to be able to comprehensively annotate the whole text; not only specific entities like Actors and Organizations but also higher-level concepts like Conditions of Applicability and Temporal Conditions, e.g.:

The added difficulty is that we have nested "entities", e.g. (from above):

<denotic> must, <temporal> within the specified period </temporal>, notify ... </deontic>


Can this still be formulated as a NER problem? If so, what would be the best type of model to solve this task assuming a dataset of ~ 50 K examples?