Update the sampled WCC example

Co-authored-by: Martin Junghanns <martin.junghanns@neotechnology.com>
Co-authored-by: Florentin Dörre <florentin.dorre@neotechnology.com>

Author: 3 people

doc/modules/ROOT/pages/algorithms/wcc.adoc

@@ -631,7 +631,7 @@ This time, we execute WCC on `myIndexedGraph` which will allow the algorithm to
 .The following will run the algorithm with sampled strategy and stream results:
 [source, cypher, role=noplay]
 ----
-CALL gds.wcc.stream('myIndexedGraph')
+CALL gds.wcc.stream('myIndexedGraph', { consecutiveIds: true })
 YIELD nodeId, componentId
 RETURN gds.util.asNode(nodeId).name AS name, componentId
 ORDER BY componentId, name
@@ -644,11 +644,11 @@ ORDER BY componentId, name
 | "Alice"   | 0
 | "Bridget" | 0
 | "Charles" | 0
-| "Doug"    | 3
-| "Mark"    | 3
-| "Michael" | 3
+| "Doug"    | 1
+| "Mark"    | 1
+| "Michael" | 1
 |===
 --
 
-As expected, the results are the same as before.
+Here we also demonstrate the use of `conscutiveIds: true`, the results are semantically the same as before.
 However, on larger graphs, there will be a notable improvement in compute time.