Extracting structured information from tables in scientific literature is a critical yet challenging task for building domain-specific knowledge bases. This paper addresses extraction of 5-ary polymer property tuples: (POLYMER, PROP_NAME, PROP_VALUE, CONDITION, CHAR_METHOD). We introduce and systematically compare two distinct methodologies: (1) a novel two-stage Hybrid Pipeline that first utilizes Large Language Models (LLMs) for table-to-text conversion, which is then processed by specialized text-based extraction models; and (2) an end-to-end Direct LLM Extraction approach. To evaluate these methods, we employ a systematic, domain-aligned evaluation setup based on the expert-curated PoLyInfo database. Our results demonstrate the clear superiority of the hybrid pipeline. When using Claude Sonnet 4.5 for the linearization stage, the pipeline achieves a score of 67.92% F1@PoLyInfo, significantly outperforming the best direct LLM extraction approach (Claude Sonnet 4.5 at 56.66%). This work establishes the effectiveness of a hybrid architecture that combines the generative strengths of LLMs with the precision of specialized supervised models for structured data extraction.