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This paper proposes Scene Abstraction, a framework for constructing structured representations of the interpretive scenes that words evoke in context, using few-shot prompting of large language models. The authors introduce COCA-Scenes, a dataset of 520 usage instances, and provide empirical evidence that scenes are reliably identifiable and align better with human interpretation than alternatives.