Yes but it doesn't generalize very well. Even on simple features like gender. If you go look at embeddings you'll find that man and woman are neighbors, just as king and queen are[0]. This is a better explanation for the result as you're just taking very small steps in the latent space.

Here, play around[1]

  mother - parent + man = woman
  father - parent + woman = man
  father - parent + man = woman
  mother - parent + woman = man
  woman - human + man = girl

Or some that should be trivial

  woman - man + man = girl
  man - man + man = woman
  woman - woman + woman = man
  

Working in very high dimensions is funky stuff. Embedding high dimensions into low dimensions results in even funkier stuff

[0] https://projector.tensorflow.org/

[1] https://www.cs.cmu.edu/~dst/WordEmbeddingDemo/

Shouldn't this itself be a part of training?

Having set of "king - male + female = queen" like relations, including more complex phrases to align embeddings.

It seems like terse, lightweight, information dense way to address essence of knowldge.