It's a bit hard to elaborate. You have to rember that a classification problem may be generalized to more than just two dimensions, in fact it can be generalize

It's a bit hard to elaborate. You have to rember that a classification problem may be generalized to more than just two dimensions, in fact it can be generalized to however many dimensions are necessary to describe the data. In the general multidimensional cases linearly separable relates to the number and type of hyperplanes (subspaces) necessary to correctly classify the data. In this simple 2-d case the subspace is a line, so this example is linearly separable. In the 3rd dimensional case linearly separable refers to classification using a single 2-dimensional plane. For 4 dimensions with the data, you need a single 3-dimensional hyperplane for it to be linearly separable, and so on.