Referring to this paper by Prof Kazemi, Prof Poole on SimpLE model for link prediction on knowledge graph.
In page 3, the paragraph on learning SimpLE Models, I understand that we have a batch of positive triples from the knowledge graph, where for each positive triple in the batch, we generate $n$ negative triple by corrupting it. So does that mean the batch size just increased by a factor of $n$ and there are only negative examples in the batch?
I think I understand the optimization function but I don't understand how we generated the labeled batch. Clarification would help!