Computer-aided research techniques for accelerating scientific discovery in polymer (and materials) science has continued to grow in both utilization and access. There remain limitations, however, especially in the curation of data. Currently, data is primarily extracted and compiled from publications and other forms of text manually. This process can be time-consuming; and many existing forms of representation are rigid, unable to account for the evolution of data. Knowledge graphs – and ontology – provide a representation that allows for the complex nature of polymer data but still need to be populated with data from literature. Given the recent successes of large language models in interpreting massive corpora, we propose a pipeline for populating a modular knowledge graph that captures state-of-the-art polymer characterizations in combination with experimental metadata and methodology. In this work, we present different variations of this pipeline, demonstrating which configurations yield desirable and undesirable results.