Graphical modeling languages, expected to simplify systems analysis and design, present a challenge in maintaining consistency across their varied views. Traditional rule-based methods for ensuring consistency in languages like UML often fall short in addressing complex semantic dimensions. Moreover, the integration of Large Language Models (LLMs) into Model Driven Engineering (MDE) introduces additional consistency challenges, as LLM's limited output contexts requires the integration of responses. This paper presents a new framework that automates the detection and correction of inconsistencies across different views, leveraging formally defined rules and incorporating OpenAI's GPT, as implemented in TTool. Focusing on the consistency between use case and block diagrams, the framework is evaluated through its application to three case studies, highlighting its potential to significantly enhance consistency management in graphical modeling.