The increasing demand for accurate and scalable semantic enrichment of scientific publications has driven the need for hybrid frameworks that combine database-centric processing with the semantic power of Large Language Models (LLMs). We present the DETEXA+LLM Semantic Enrichment Workflow Builder, an extension of the DETEXA framework, which allows users to define, execute, and visualize hybrid text analysis workflows over relational backends. Our system supports declarative construction of pipelines that integrate regex-based pattern matching, classification, and selective LLM invocation via automatically generated Python UDFs and YeSQL queries. A visual web interface enables users to configure metadata enrichment workflows with minimal programming effort. We demonstrate the utility of our approach through three diverse and essential use cases for digital libraries: (i) classification of Data and Software Availability Statements (DAS), (ii) extraction of Protein Data Bank (PDB) codes, and (iii) mining of funding source mentions. Our results show that combining rule-based logic with targeted LLM calls improves both accuracy and efficiency, making the system suitable for large-scale scholarly metadata extraction tasks.