How can union-find algorithm be used with “real” data

Question

In the beginning of the Princeton algorithms course the Dynamic connectivity problem is presented (quick-find, quick-union). Here is how it's described:

The input is a sequence of pairs of integers, where each integer represents an object of some type and we are to interpret the pair p q as meaning “p is connected to q.” We assume that “is connected to” is an equivalence relation, which means that it is

• Reflexive : p is connected to p.
• Symmetric : If p is connected to q, then q is connected to p.
• Transitive : If p is connected to q and q is connected to r, then p is connected to r.

And it has one simple implementation called quick-find:

One approach is to maintain the invariant that p and q are connected if and only if id[p] is equal to id[q]. In other words, all sites in a component must have the same value in id[].This method is called quick-find because find(p) just returns id[p], which immediately implies that connected(p, q) reduces to just the test id[p] == id[q] and returns true if and only if p and q are in the same component...To combine the two components into one, we have to make all of the id[] entries corresponding to both sets of sites the same value, as shown in the example at right.

My question is how can this be used for real objects? It works for integers, but what if I need to know whether the object A is connected to the object B, not if 3 is connected to 5? One solution I can think of is to have an array of objects where each object's index in the array corresponds to the index of the array used for connectivity. For example:

   1   2   3   4   5
[ { } { } {A} { } {B} ]   <---- real data

   1   2   3   4   5
[  1   2   3   4   3 ]    <---- connections (3 and 5 have the same group id 3)

Is that how it's applied?