NER with Unsupervised Learning?
First, when we as humans interpret "Fyonair is from Fuabalada land" we use our knowledge of "is from" and "X land" to infer that Fyonair is probably a person and Fuabalada land probably a location. Therefore our process is not (at least not completely) unsupervised: we have seen this sentence structure before ("training") and we use our "model" to "predict" the two entities. 

A good (supervised) NER is trained to recognize this kind of patterns as well. The example that you mention might be a bit too hard, but with something like "Dr Fyonair is the new CEO of Fubalada" a decent NER should be able to recognize that "Dr Fyonair" is a person (thanks to the "Dr") and that "Fubalada" is a company (thanks to the "CEO of") even though it has never seen these particular proper names.

So a standard (supervised) NER is supposed to recognize entities it has never seen before, provided it has clues in the sentence about them. Technically it's not really a state of the art NER if it recognizes only the entities that it has seen during training, it's just a string matching program. It's true that NERs make a lot of errors with entities which haven't been seen previously, but that's simply because they are harder to catch in general.