Legal text is hard for named entity recognition. New statutes, parties and courts appear continually, and expert annotation is costly. Open-world NER can assign types that were not fixed at training time. These systems are built on general-domain text, though, and legal uses adapt them corpus by corpus and language by language. We take the opposite route, and apply OPENER unchanged, a frugal pipeline of ready-to-use components. Its entity embedder stays generic, and no part of it is ever fitted to a legal schema. We test its two typing heads on four legal corpora, in English, Portuguese and German. One needs no target label at all, the other fits a small linear probe. We compare them against GLiNER, GNER, OWNER and a 4-bit Qwen, on quality, latency and energy at once. The transfer holds in English, and weakens across languages. The label-free head degrades sharply. The supervised probe is barely affected, and scores best overall (35.9 AMI end-to-end, 61.4 on gold mentions). Both stay one to two orders of magnitude cheaper than the generative baselines. Detection, not typing, is what caps every system here. The English detectors recover as little as 10.2% of the German mentions. On the ones they do find, our supervised head assigns the right type 78.7% of the time on average over the four corpora, against 64.6% for the detector alone. Yet typing all of them correctly would still stop at 70 AMI out of 100. Where labels exist, the supervised head is the practical operating point, and in-language detection is where adaptation should go first.