Commonsense visual sensemaking requires robust mechanisms to extract scene elements and other visual features from perceived imagery, as well as rich conceptual commonsense knowledge and reasoning to interpret the perceived interactive dynamic. In this context, we position ongoing work on using integrated Vision Language Models (VLM) and Answer Set Programming (ASP) based reasoning about (dynamic) spatial configurations using VLMs as a neural foundation for symbolic modeling of dynamic scene structures. We sketch preliminary results highlighting practical usability by application to the task of Visual Question Answering (VQA) with the STRIDE-QA driving dataset.