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.

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).

Most labels have definitions you can access using spacy.explain(label).

For NORP: "Nationalities or religious or political groups"

For more details you would need to look into the annotation guidelines for the resources listed in the model documentation under https://spacy.io/models/.

In spaCy 3 do nlp.get_pipe('ner').labels.