Difference between Global Outlier and Contextual Outlier?
Let's say that your dataset is a bunch of temperatures with a bunch of features related to when/where the measurements were made.
A measurement is a global outlier if it diverges from the distribution of temperatures regardless of the features (when and where) because that measurement is far off the global distribution (100°C for example).
The example makes a good case of how looking at conditional distributions (temperature|place, time of measurement) can provide additional insights (here detecting that 28°C is a contextual outlier). Contextual outliers are within the global distribution but are outliers when looking at conditional distributions.
Now of course your dataset has some context, so in a sense you're right, everything is contextual, but the global/contextual border resides in what you have at hand : if all you have is the temperatures from one day, comparing a temperature from that day to all the measurements of that day will allow you do detect gloabl outliers ; now if you had access to temperatures all year long and that your still looked at that day precisely, that would become a conditional distribution.