Making sense of natural language and narratives requires building and manipulating a situation model by adding incoming information to the model and using the context stored in the model to comprehend subsequent details and events. Situation model maintenance is supported by the default mode network (DMN), but comprehension of the individual moments in the narrative relies on access to the conceptual store within the semantic system. The present study examined how these systems are engaged by different narrative content to investigate whether highly informative, or semantic, content is a particularly strong driver of semantic system activation compared with contextually driven content that requires using the situation model, which might instead engage DMN regions. The study further investigated which subregions of the graded semantic hub in the left anterior temporal lobe (ATL) were engaged by the type of narrative content. To do this, we quantified the semantic, pragmatic, social, ambiguous, and emotional content for each sentence in a complete narrative, the English translation of The Little Prince. Increased activation in the transmodal hub in the ventral ATL was only observed for high semantic (i.e., informative) relative to low semantic sentences. Activation in the dorsolateral and ventrolateral ATL subregions was observed for both high relative to low semantic and social content sentences, but the ventrolateral ATL effects were more extensive in the social condition. There was high correspondence between the social and pragmatic content results, particularly in the ventrolateral ATL. We argue that the ventrolateral ATL may be particularly engaged by internal, or endogenous, processing demands, aided by functional connections between the anterior middle temporal gyrus and the DMN. Pragmatic and social content may have driven endogenous processing given the pervasive and plot-progressing nature of this content in the narrative. We put forward a revised account of how the semantic system is engaged in naturalistic contexts, a critical step toward better understanding real-world semantic and social processing.