I don't know how the verb "to train" became the technical shorthand for running gradient descent on a large neural network. But that's orthogonal to the fact that these stories are very, very likely part of the training dataset and thus something that the network is optimized to approximate. So no matter how technical you want to be in wording it, the fundamental irony of cautionary tales (and the bad behavior in them) being used as optimization targets remains.