questions bearing a surface form different from the source sentence but which are driven by reasoning rather than just being paraphrastic. For example, in the sentence, “Brutus murdered Julius Caesar”, the event [*murder-42.1*](https://uvi.colorado.edu/verbnet/murder-42.1) entails a final state of “death” or the *Patient* participant not being alive at the end of the event. So, we construct a template “mainAux the Patient otherAux not alive?”. Similarly, the event [*pay-68-1*](https://uvi.colorado.edu/verbnet/pay-68-1) results in a final state in which the *Recipient* “Perry” has possession of “\$100” and the *Agent* “John” has possession of “the car”, against which we define the templates as shown in Figure \[fig:vn1\].

We formulate two sets of questions: boolean type and which-type questions asking specifically about these states. We create templates for VerbNet’s stateful predicates like **has\_location, has\_possession, has\_information, seem, has\_state, cost, desire, harmed, has\_organization\_role, together, social\_interaction, authority\_relationship,** etc. which are present in 64.4% of the member verbs in VerbNet[^2]. We outline a few of the templates in Table \[vn-table\].

During inference time, we first compute the VerbNet sense, the associated thematic role mapping, and syntactic frame (along with the predicates) with the help of @brown2019verbnet’s parser. VerbNet’s predicates are governed by the sub-events in which they occur. Although VerbNet’s representation 