Some questions about GoogleNet paper
"Higher dimensional representation" refers to having more feature maps like you suggested (check the inception module proposed in figure 7).

As to what disentangled features mean, I believe they mean decorrelated: the more features (with different filters) the inception module extracts, the more and the faster the network learns. Because the network will have access to more information leading to detecting which features are salient earlier in the training.