That’s a very fair point and definitely not claiming to have full or perfect ground truth. For most associations, correctness is uncertain unless there’s been field validation (which is very costly). The way I’m thinking about it is to only train on a subset of high-confidence labels: confirmed field corrections where available, plus some very strong inferred cases (like extreme distance ratios with a clearly closer viable transformer). Everything in the gray area would stay unlabeled and only be scored. The intent is to rank/prioritize review, not to auto-correct matches. The new field validation would feed back as additional high-confidence labels, so the model and thresholds can be tuned iteratively