This talk presents a formal methodology for constructing a Multi-Modal Knowledge Graph for a smart city, addressing data privacy and heterogeneity by using entirely synthetic data. We demonstrate a Python pipeline that leverages Large Language Models for text generation and knowledge extraction, Pandas for sensor data simulation, and rdflib for graph construction. The result is a robust, privacy-preserving foundation for a Cognitive Digital Twin, enabling advanced urban analytics.