As of very recently, we have observed a convergence of technologies that have led to the emergence of lifelogging as a potentially pervasive technology with many real-world use cases. While it is becoming easier to gather massive lifelog data archives with wearable cameras and sensors, there are still challenges in developing effective retrieval systems. One such challenge is in gathering annotations to support user access or machine learning tasks in an effective and efficient manner. In this work, we demonstrate a web-based annotation system for sensory and visual lifelog data and show it in operation on a large archive of nearly 1 million lifelog images and 27 semantic concepts in 4 categories.