Well, if you want to get a really good accuracy, then you need a model specific to your domain - which most likely you'll need to train up yourself.
The NLP systems leading in competitions such as CONLL conference tend to be publicly available, so you can get a "general purpose" system there; but the current way usually is to train specific model for a specific purpose - since if you don't have a predetermined purpose, you can't really tell which of items should be tagged as places (instead of things); you can't tell which things should be tagged as 'things' and in what way they should be classified deeper - the list of classes tends to be application-specific.
The NLP systems leading in competitions such as CONLL conference tend to be publicly available, so you can get a "general purpose" system there; but the current way usually is to train specific model for a specific purpose - since if you don't have a predetermined purpose, you can't really tell which of items should be tagged as places (instead of things); you can't tell which things should be tagged as 'things' and in what way they should be classified deeper - the list of classes tends to be application-specific.