The statistical pipeline components like ner provide their labels under .labels:

import spacy
nlp = spacy.load("en_core_web_sm")
nlp.get_pipe("ner").labels

The NER label scheme varies by language, and depends heavily on what kind of training data was available. You need to check the "Label Scheme" entry on the model page, which should have an NER section. For example, here's Japanese.

Short answer:

No, you cannot restrict NER to not tag specific Tags or the opposite.

What you can do is limit it in code or modify the model [see long answer].

Limiting it in code is just filtering the retrieved entities, but it won't solve your problem with missclassifications.

import spacy

nlp = spacy.load("en_core_web_sm")
doc = nlp(u"Apple is looking at buying U.K. startup for $1 billion")

entities = [ent for ent in doc.ents if ent.label_ == "ORG"]

Long answer:

You can restrict NER in spacy, but not with a simple parameter (currently).

Why not? Simple: NER is a supervised machine learning task. You provide text with tagged entities, it trains and then attempts to predict new instances from the parameters it learned beforehand.

If you want NER only to recognize certain entities, such as orgs, you have to train a new model only with org instances.

If you're familiar with Machine Learning concepts, you'll understand it this way: in a multi class classification task, you cannot simply remove a class without retraining the entire model with filtered train data.

Check this page for more info on NER training: https://spacy.io/usage/linguistic-features/#named-entities

If you check the page for a pipeline you'll see the data sources listed. For the NER data in the English pipelines OntoNotes is used. The schema is documented in the OntoNotes Manual, for example:

PERSON People, including fictional
NORP Nationalities or religious or political groups
FACILITY Buildings, airports, highways, bridges, etc.
ORGANIZATION Companies, agencies, institutions, etc.
GPE Countries, cities, states
LOCATION Non-GPE locations, mountain ranges, bodies of water
PRODUCT Vehicles, weapons, foods, etc. (Not services)
EVENT Named hurricanes, battles, wars, sports events, etc.
WORK OF ART Titles of books, songs, etc.
LAW Named documents made into laws 
LANGUAGE Any named language 

In spacy you can get these definitions using spacy.explain, like spacy.explain("FACILITY"). Sometimes the official documentation has more detailed explanations, though in this case it seems not to.

"train station" is not picked up because it is not a named entity - named entities are typically proper nouns, not common nouns.

Also note the model is not perfect and it will make mistakes, and it is hard to explain individual mistakes (see here).