Post-market adverse drug effects (ADEs) are often found in the clinical notes of Electronic Health Records. We evaluate open-weight large language model, Llama-3.1, for detection of ADEs, Named Entity Recognition, and Entity Linkage tasks across varying prompting techniques suited for text decomposition including, but not limited to zero-shot, few-shot and tree of thought. Finally, we propose a novel improvement to the tree of thought prompting technique which combines consistency and thought merging. Our proposed approach is then evaluated and compared with the other approaches on these tasks using standard metrics for each task (f1, precision and recall).