Leveraging the power of symbolic knowledge representation, this study compares outcomes of natural language interaction with BPMN model serializations, examining semantic graphs derived from RDF export of BPMN provided by Bee-Up, in contrast to the conventional BPMN XML export from the process modeler of SAP Signavio. By prompt engineering, we investigate the proficiency of certain GPT services of OpenAI in navigating the semantic intricacies of RDF and the structural hierarchy of XML, ultimately illuminating implications for knowledge retrieval. The findings delve into the complexities of querying BPMN representations using natural language, revealing the transformative capabilities of RDF, but also the value of BPMN employed as a schema for procedural knowledge graphs—i.e., shifting away from their traditional role as diagrams or automation configurators. As per the experimental results, the RDF export showcases superior richness for natural language queries as the graph-like structure of visual diagrams is closer to semantic networks than to XML tag structures, carrying implications for Business Process Management.