Reviewer has chosen to be Anonymous

Overall Impression: Average

Content:
Technical Quality of the paper: Average
Originality of the paper: Yes, but limited
Adequacy of the bibliography: Yes

Presentation:
Adequacy of the abstract: Yes
Introduction: background and motivation: Good
Organization of the paper: Needs improvement
Level of English: Satisfactory
Overall presentation: Average

Detailed Comments:

In the new version, several of the previously criticized issues have been well addressed, e.g. there are now some clearer research questions with a better motivation and the reasons for constructing the particular test cases used are made substantially clearer.

What is still not clear to me is why the scores for e.g. semantic faithfulness need to be automatically computed instead of assessing them manually (this could be done by some independent expert). Given the rather small number of answers to compare, this should be easily feasible and I would find it much more meaningful... It would also allow the discussion of examples to be shortened to their essence, avoiding explanation of scores that does not contribute much in my opinion. This should be corrected before final publication.

Reviewer has chosen not to be Anonymous

Overall Impression: Good

Content:
Technical Quality of the paper: Good
Originality of the paper: Yes
Adequacy of the bibliography: Yes

Presentation:
Adequacy of the abstract: Yes
Introduction: background and motivation: Good
Organization of the paper: Satisfactory
Level of English: Satisfactory
Overall presentation: Good

Detailed Comments:

The revised paper presents a significant improvement over the original submission, addressing nearly all prior concerns. The methodology is now clearly described, and the research questions are explicitly stated. The authors have strengthened the literature review, added a discussion section, and refined the conclusion with future research directions.

The experimental design is thorough, using a well-established assessment framework (RAGAs) to evaluate GPT-4’s ability to analyze BPMN process serializations. The comparison between XML and RDF-based representations is well-motivated and methodologically rigorous.

Remaining concerns:
1. The presentation remains highly technical – the methodology and experiment sections contain dense details that may be difficult for non-expert readers to follow. Some figures, tables, and long technical explanations could be moved to an appendix or made more concise.
2. Limited discussion of limitations – the paper would benefit from a more explicit reflection on the study’s constraints, such as the reliance on GPT-4 only and the potential variability in responses due to the stochastic nature of LLMs.
3. Practical implications could be expanded – the discussion on how these findings impact BPM practitioners and tool developers could be more detailed.

Overall, this is a strong paper with minor revisions needed for improved clarity and accessibility.