Company name retrieval and final entity clustering can respond differently to encoder adaptation. We study this dependency through a controlled pipeline: train multilingual E5 encoders with matched positive examples and random or hard negatives, select checkpoints on development data, retrieve within equal country-specific edge budgets, and apply unchanged pair classifiers and guarded Union-Find clus- tering. Evaluation uses a subsequently collected GLEIF snapshot; a stratified, assisted human audit and a post-hoc temperature sweep examine label and parameter sensitivity. On 3,633 names with source-silver labels, adapted retrieval raises direct pair recall from 53.74% to 76.98%, whereas cluster recall moves from 32.54% to 33.33%. Most newly retrieved positives fail the existing classifier threshold. The study contributes a controlled comparison and pair-level diagnosis of this loss between retrieval and confirma-tion. It shows why evaluating a replacement retriever requires measuring the resulting entity partition, while leaving joint matcher retraining and independent gold-label evaluation for future work.

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