A true positive is an outcome where the model correctly predicts the positive class. Similarly, a true negative is an outcome where the model correctly predicts the negative class. A false positive is an outcome where the model incorrectly predicts the positive class.
The hungry wolf turns the flock into lamb chops. The town goes hungry. Panic ensues. Let's make the following definitions: "Wolf" is a positive class. "No wolf" is a negative class.