added explanation for spurious case

constants.py

@@ -27,13 +27,14 @@ Comparing the ground truth and predicted span objects we get the following broad
 
 ##### Comparison Cases
 
-| Category        | Explanation                                                              |
-| --------------- | ------------------------------------------------------------------------ |
-| Correct (COR)   | both are the same                                                        |
-| Incorrect (INC) | the output of a system and the golden annotation don’t match             |
-| Partial (PAR)   | system and the golden annotation are somewhat “similar” but not the same |
-| Missing (MIS)   | a golden annotation is not captured by a system                          |
-| Spurious (SPU)  | system produces a response which doesn’t exist in the golden annotation  |
+| Category        | Explanation                                                                             |
+| --------------- | --------------------------------------------------------------------------------------- |
+| Correct (COR)   | both are the same                                                                       |
+| Incorrect (INC) | the output of a system and the golden annotation don’t match                            |
+| Partial (PAR)   | system and the golden annotation are somewhat “similar” but not the same                |
+| Missing (MIS)   | a golden annotation is not captured by a system                                         |
+| Spurious (SPU)  | system produces a response which doesn’t exist in the golden annotation (Hallucination) |
+
 
 The specifics of this categorization is defined based on our metric of choice. For example, in some cases we might want to consider a partial overlap of offsets correct and in other cases incorrect. 
 Based on this we have the Partial & Exact span based criterias. These categorization of these schemas are shown below