In the context of an inference step, information can play the role of antecedent (also called premise) and conclusion.
The use of PML in subsequent projects evolved the language in new directions broadening its capability to represent provenance knowledge beyond the realm of ATPs and automated reasoning.
Enhancements were also required to further understand motivation behind the need of automated theorem provers to derive conclusions: new capabilities were added to annotate how information playing the role of axioms were attributes as assertions from information sources; and the notion of questions and answers were introduced to the language to explain to a third-party agent why an automated theorem prover was used to prove a theorem (i.e., an answer) from a given set of axioms.
The first version of PML (PML1) was developed at Stanford University's Knowledge Systems Laboratory in 2003 and was originally co-authored by Paulo Pinheiro, Deborah McGuinness, and Richard Fikes.
[1] The second version of PML (PML2) developed in 2007 modularized PML1 into three modules to reduce maintenance and reuse cost: provenance, justification, and trust relations.