Those would really just be identifiers. I think the key property of embeddings is that the dimensions each individually mean/measure something, and therefore the dot product of two embeddings (similarity of direction of the vectors) is a meaningful similarity measure of the things being represented.
The classic example is word embeddings such as word2vec, or GloVE, where due to the embeddings being meaningful in this way, one can see vector relationships such as "man - woman" = "king - queen".
The classic example is word embeddings such as word2vec, or GloVE, where due to the embeddings being meaningful in this way, one can see vector relationships such as "man - woman" = "king - queen".