Hi!
To be honest, that doesn't really sound like a textcat challenge to me. Text classification is more about assigning one or multiple labels to the entire text. From what you described, you're still interested in finding the actual token boundaries of your 5 types of entities, correct?
That said, you're right that this doesn't sound like a "traditional" NER challenge like recognizing properly capitalized names or cities. But you still need something similar to NER to detect the token boundaries + label them. The good news is we've recently added support in spaCy for a new type of component called spancat that does exactly that. You can read a bit more here: https://spacy.io/usage/v3-1#spancategorizer
The spancat component has less restrictions than the NER: it allows overlapping/nested spans, for instance. Traditionally, with NER, one token can belong to only one named entity, but with the spancat a token can belong to multiple spans. You might not need this specific feature for your specific use-case, but it might still be worth experimenting whether the spancat component gives you better results than the ner when training.
The upcoming Prodigy 1.11 will provide built-in support for training a spancat component directly within Prodigy, but you might already experiment with spaCy 3.1 to check out this (experimental) functionality. I can't say up-front whether or not it'll improve your NER performance results, though. But it's worth a shot!
PS: not sure what the use if of your "OTHER" label? It feels like an unnecessary burden during annotation, and I'm not sure it actually helps with performance?