Deep learning is increasingly applied to intraoperative and surgical video analysis to enable real-time workflow recognition, and decision support for improved surgical precision. A key direction is modeling surgical activity as triplets of instrument, action, and target, which provide a richer representation of procedures. However, existing approaches often depend on bounding-box annotations or lack temporal context. We propose TWiST (Temporal Weakly Supervised Triplet detection), a framework that combines weakly supervised instrument localization, temporal attention for triplet prediction, and grounding of triplets with detected instruments. Our experiments show that TWiST outperforms prior weakly supervised baselines.