As part of developing a Computational Narrative Understanding, modeling events within stories has recently received significant attention within the digital humanities community. Most of the current research aims at good performance when predicting events. By contrast, we explore a focused approach based on qualitative observations. We attempt to trace the role of structural elements – more specifically, temporal function words – that may be characteristic of a narrative's turning point. We draw on a corpus of UFO sighting reports in which authors employ a prototypical narrative structure that relies on a turning point at which the extraordinary intrudes the ordinary. Using binary logistic regression, we can identify structural properties which are indicative of turning points in our data, showcasing that a focus on detail can fruitfully complement NLP models in gaining a quantitatively informed understanding of narratives.