Graph Neural Networks (GNNs) excel at learning from interconnected data, but they sometimes struggle to grasp the nuances of text associated with nodes. Furthermore, picking the *right* GNN for a specific task often feels like a guessing game. Researchers are exploring a fascinating new approach: using Large Language Models (LLMs) as intelligent ensemblers for multiple GNNs. Imagine having an AI that can strategically combine the strengths of several specialized GNNs, leading to more accurate and robust predictions. This is the promise of LensGNN, a novel model that first aligns the outputs of different GNNs into a common space. Then, using a technique called LoRA, it fine-tunes an LLM to understand both the GNN outputs (representing graph structure) and the textual node attributes. Essentially, LensGNN teaches the LLM to become a master conductor, orchestrating the individual GNNs to produce a harmonious and accurate prediction. Experiments on various benchmark datasets show LensGNN outperforming individual GNNs and even other LLM-enhanced graph learning methods. This suggests that LLMs could be the key to unlocking the full potential of GNNs, especially when dealing with complex, text-rich graph data. While promising, challenges remain. Fine-tuning large LLMs requires significant computational resources, and prompt engineering plays a crucial role in guiding the LLM's learning process. Future research could explore more efficient fine-tuning techniques and automated prompt generation strategies. LensGNN represents an exciting step towards a future where LLMs and GNNs work together seamlessly, leading to more powerful AI systems capable of tackling complex real-world problems.