Engineering organisations increasingly aim to reuse historical BOM, CAD, and requirements data to identify recurring components. A key prerequisite is Entity Matching (EM), whose performance on heterogeneous engineering data is unclear. This paper evaluates classical models, zero-shot LLMs, and hybrid EM on Amazon–Google and a multimodal engineering dataset. Random Forest and XGBoost achieve near–state-of-the-art results; LLMs perform well but are costly, hybrids add little. EM transfers under controlled conditions and forms a foundation for reference architecture reconstruction.
