Entity matching (EM) is essential for connecting data across sources, particularly in sensitive domains like human trafficking investigations. However, research faces a critical gap: the lack of realistic gold standard datasets containing personal identifying information. This paper introduces a methodology for creating gold standard datasets, demonstrated through the development of a representative dataset for personal identification information (PII). Our approach combines multiple EM techniques to identify candidate matches, followed by a systematic annotation and validation process. Notably, our findings demonstrate that different techniques identify largely non-overlapping sets of matches, validating the need for our multi-technique methodology. Our approach provides a reproducible template for creating gold standard datasets in domains where realistic evaluation resources are scarce.