The ability to accurately pinpoint the location of an event (e.g. loss, fault or bug) is of fundamental requirement in many systems. While we have state-of-the-art models to predict likelihood of an outcome, being able to pinpoint to the entity responsible for the outcome is also important. For example, in an e-commerce setup, a lost package detection system needs to infer the reason or location (delivery station, sort center, trucks) in case of a missing item, a network management system would like to diagnose nodes that are faulty based on end-end packet flow traces or a compiler needs to point out the exact location of a code that is erroneous. In this paper, we present an Attention based neural architecture for entity localization to accurately pinpoint the location of package loss in delivery network and bugs in erroneous programs. Our model performs well in scenarios where there is no annotation / ground truth for entities for localization. It can also adapt itself if annotations / ground truth is available for even a subset of entities by leveraging semi-supervision. The core of our model is a ladder-style architecture that helps us achieve state-of-the-art performance in both entity localization and detection. Further, to show the generality of our approach, we demonstrate its performance on a bug localization task for software programs. On a publicly available data-set, our solution outperforms the state-of-the-art technique by a significant margin.