We describe a method, based on Jennifer Nado's proposal for classification procedures as targets of conceptual engineering, that implements such procedures by prompting a large language model. We apply this method, using data from the Wikidata knowledge graph, to evaluate stipulative definitions related to two paradigmatic conceptual engineering projects: the International Astronomical Union's redefinition of PLANET and Haslanger's ameliorative analysis of WOMAN. Our results show that classification procedures built using our approach can exhibit good classification performance and, through the generation of rationales for their classifications, can contribute to the identification of issues in either the definitions or the data against which they are being evaluated. We consider objections to this method, and discuss implications of this work for three aspects of theory and practice of conceptual engineering: the definition of its targets, empirical methods for their investigation, and their practical roles. The data and code used for our experiments, together with the experimental results, are available in a Github repository.

翻译：我们描述了一种基于Jennifer Nado提出的分类程序作为概念工程目标的方法，该方法通过提示大型语言模型来实现此类程序。我们运用此方法，结合维基数据知识图谱的信息，评估了两个典型概念工程项目相关的约定定义：国际天文学联合会对"行星"的重新定义以及Haslanger对"女性"的改良分析。实验结果表明，采用本方法构建的分类程序展现出良好的分类性能，并且通过生成分类依据，有助于识别定义本身或评估数据中存在的问题。我们探讨了针对此方法的质疑，并讨论了本工作对概念工程理论与实践的三个层面的启示：目标定义、实证研究方法及其实际作用。实验所用数据与代码及实验结果已发布于Github存储库。