The task of knowledge graph completion poses significant challenges due to the vast scale and complexity of relationships that need to be predicted within large datasets. Introducing a novel concept of partial dormancy within the GPT-Neo architecture offers a significant advancement by enabling selective neuron activation, thereby reducing computational overhead while maintaining high accuracy. Through dynamic gating mechanisms, the model efficiently handles diverse input complexities, resulting in optimized performance across various knowledge graph sizes. The model's ability to balance computational efficiency with high accuracy highlights its potential for deployment in resource-constrained environments. Experimental results demonstrated that the partially dormant GPT-Neo outperforms fully active models in both efficiency and scalability, suggesting a transformative approach for future applications in knowledge graph-related tasks. The contributions made extend beyond enhancing model efficiency, providing a foundational framework that may influence the development of more adaptable and energy-efficient language models.