Well, the nature of NER is that it can extract 'Steve Jobs' as either a PERSON or a BOOK entity (in my case). You can in principal extract whatever kind of enti

Well, the nature of NER is that it can extract 'Steve Jobs' as either a PERSON or a BOOK entity (in my case). You can in principal extract whatever kind of entity you want, provided you have appropriate training data. The idea (loosely) is that it learns the structure of text around, say, a book. You can then label a piece of text as a book, despite not previously knowing that title. It doesn't solve the problem of two books having identical titles, however :). (Note: I'm not an expert in NER/NLP, so please correct me if I have got something wrong!)