This paper presents a structured reasoning pipeline that integrates Large Language Models (LLMs) with a tri-layered knowledge graph (KG) framework to automate the generation of SysML v2 modeling artifacts from structured requirements. The approach addresses the persistent challenge of LLM hallucinations in model-based systems engineering (MBSE) by grounding generative outputs in curated knowledge sources. Two core KGs are constructed using a custom Python-based Reasoning Engine: one encodes reusable SysML patterns annotated across nine diagram types, and the other captures domain-specific system models (e.g., aerospace, automotive). A third, system-specific KG is automatically synthesized by parsing capability-linked requirements and aligning them with the foundational KGs and structured prompt templates. These KGs enable context-aware, hierarchical system model generation and inform a dual-parameter prompting strategy: one set is auto injected post-KG construction to preserve domain integrity, and another is derived through our Reasoning Engine over system data prior to LLM invocation. The resulting prompts are validated and refined to produce accurate, high-fidelity SysML v2 representations. This framework offers a scalable path for integrating generative AI into digital engineering (DE) workflows. Ongoing work includes semantic alignment, ontology integration, traceability, prompt optimization, and the addition of a knowledge graph-based memory and planner to enable our reasoner to efficiently execute step-by-step plans—further advancing AI-assisted MBSE.