Effect axioms constitute the cornerstone of formal theories of action in AI. They drive standard reasoning tasks, especially prediction. These tasks need not be coupled with actual acting; the reasoning agent is, thus, typically given an ex post acto narrative of what actions took place. An acting agent, however, has no access to such knowledge; it needs to face what we call the event categorization problem, and figure out what actions it did. Until this is achieved, effect axioms will be useless. A careful review of the literature on effect axioms reveals that their syntax, semantics, and ontological commitments are so deeply entrenched in the armchair reasoning about action paradigm, that they cannot be used in resolving the event categorization problem. By enriching the ontology of action theories, we propose a different approach for representing effects of actions that unifies the two views. The enriched ontology is independently motivated by linguistic concerns.