Conversational data are central to the study of interaction dynamics and social structures across psychological research. However, constructing structured social networks from unstructured conversational data remains a major methodological challenge. This study presents a pipeline for network construction using prompt engineering. We employ an ensemble of five Large Language Models (LLMs) with majority voting to automate utterance classification, reducing dependence on manual coding without task-specific parameter fine-tuning. Classification uncertainty is assessed through an uncertainty quantification framework based on Shannon entropy, which can be used to prioritize ambiguous cases for review. The classified utterances are used to construct directed interaction networks for subsequent analysis. Reliability and accuracy are established relative to the criterion-referenced and human comparisons reported here rather than as unconditional guarantees across settings. We demonstrate the utility of this approach through two illustrative applications to classroom interaction data: network centrality analysis to characterize participant roles, and network mediation analysis using the additive and multiplicative effects network (AMEN) model to examine how interaction structures mediate the relationship between gender and mathematics performance. This pipeline provides a scalable foundation for automated network construction from conversational data across diverse research contexts.

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