Predictive Process Monitoring (PPM) leverages historical data to forecast information about ongoing business processes. Recent methods have utilized advanced deep learning and classical machine learning models. However, the role of semantic information that can be extracted from event logs has been underexplored, although such information has been demonstrated to have significant advantages for other process mining tasks, such as anomaly detection. Therefore, this paper proposes a novel mechanism that aims to exploit semantic information for PPM, particularly by extracting information regarding the status of business objects associated with process instances from event data. We evaluate this mechanism in outcome-oriented and next activity prediction tasks, using state-of-the-art large language models (LLMs) for semantic extraction. Our results show that integrating semantic information improves prediction performance across these tasks. This work demonstrates that utilizing semantic information in PPM has considerable potential, especially in combination with advanced language models.