What is the actual output of Principal Component Analysis?
Have you tried reading the Intuition section of the PCA page on wiki? Also, the Further Considerations section, I think, explains what the various components represent and addresses your questions.

A short summary of what those sections describe is that the original components (i.e., axes) of the data do not properly represent the relationship inherit in the data. Instead, the components can be combined to yield a new set of components, or axes, that better describe how the data is distributed.

The wiki link states that "PCA can be thought of as fitting an n-dimensional ellipsoid to the data". In that line of thinking, the output of PCA is the eigenvectors and eigenvalues that define the orientation and length of the ellipsoid that best fits the data. 

What you do with that ellipsoid is up to you and your application of PCA.