The ability to automatically identify whether an entity is referenced in a future context can have multiple applications including decision making, planning and trend forecasting. This paper focuses on detecting implicit future references in entity-centric texts, addressing the growing need for automated temporal analysis in information processing. We first present a novel dataset of 19,540 sentences built around popular entities sourced from Wikipedia, which consists of future-related and non-future-related contexts in which those entities appear. As a second contribution, we evaluate the performance of several Language Models including also Large Language Models (LLMs) on the task of distinguishing future-oriented content in the absence of explicit temporal references.