Machine Learning Flash cards

I think this answer is incorrect, or maybe my understanding is wrong, but isn't it so that TN refers to instances that are correctly predicted as negative, that is, when the actual situation is negative (no event, no disease, etc.) and the prediction is also negative? This is a correct prediction, not an error.

Type I error is actually related to False Positives (FP), which refer to the situation where a negative case is wrongly predicted as positive. In other words, a Type I error occurs when no event has occurred, but the model predicts that an event will occur.

This expression is fluent and accurately describes the relationship between True Negatives and Type I error, as well as their connection with False Positives.
Please reply my confusion, appreciate !