Thanks for the suggestion! I agree that a video on this would be a great idea, I'll start thinking about that 
. I've been weighing up different ideas for videos and I think that's a great suggestion.
There are two ways to do the text-classification-as-NER strategy. One is to structure your downstream application so that you don't require the specific highlighted span. Sometimes this is viable, sometimes it isn't.
The other way is to chain together text classification and some sort of span identification strategy. You can either put the text classifier first or second here. The text classification label indicates whether the sentence contains any instances of the named entity in question. This can make life much easier for the downstream NER model, as it doesn't have to worry about confusing instances that have nothing to do with what you're trying to recognise.
The other approach is to run a more generic span identification process first, for instance by classifying with a single label. You then use text classification to provide the more specific labels you might be trying to recover.
The text classification approach works best if you usually only have one candidate span per sentence, or per other easily segmentable unit of text. If you have multiple candidate spans as in your example, it's a bit trickier.
Sorry I can't give more specific advice: it's inherently pretty heuristic driven, based on experiments and the characteristics of your problem.