The growing need for scalable research resource discovery in digital libraries has motivated the development of frameworks that combine traditional processing with large language models (LLMs). Building upon DETEXA, a declarative and extensible SQL-based text analytics framework, we propose a key extension: the integration of LLMs through User-Defined Functions (UDFs). This architecture preserves the scalability and declarative nature of SQL workflows while selectively invoking LLMs for tasks that require deep semantic understanding. To balance the trade-off between efficiency and precision, we explore three processing alternatives: LLM-only pipeline, pattern-based approach, and a hybrid model with selective LLM invocation. As a running example, we focus on the extraction of Data and Software Availability Statements from research publications, demonstrating the benefits of hybrid semantic enrichment. Experimental results show that the proposed approach achieves significantly improved accuracy over pattern-based methods, while maintaining runtime performance compared to full LLM-based solutions. Our framework offers a path toward efficient, extensible, and semantically enhanced text analytics pipelines.