Generally overfitting means that you give your fit too many degrees of freedom (by trying too many things out or optimising over a too big space), as a result of which the fit won't generalise well and the training loss of your chosen/"optimal" fit will be much lower than the validation loss (although occasionally and accidentally you may find a good generalisable fit when doing overfitting).
Overfitting is not a binary yes/no thing. What happens in your images is that if you optimise training loss/accuracy you find a fit that has a validation loss/accuracy that is worse (than training) and not optimal (compared to other validation results). So with stopping earlier, i.e., "trying out less" - not sure what the numbers on your x-axis actually mean - you could've found something that is better regarding validation loss. So trying out more has "masked" something that would've been better. That's overfitting.
The story is not quite clear cut here though, because the actual optima of the training loss/accuracy seem to appear at points where the validation loss/accuracy isn't that bad either (if I see things correctly), so there is some overfitting but it may not hit that badly here and may be tolerable. As I said, it's not always black or white.