  The life trajectories of notable people have been studied to pinpoint the
times and places of significant events such as birth, death, education,
marriage, competition, work, speeches, scientific discoveries, artistic
achievements, and battles. Understanding how these individuals interact with
others provides valuable insights for broader research into human dynamics.
However, the scarcity of trajectory data in terms of volume, density, and
inter-person interactions, limits relevant studies from being comprehensive and
interactive. We mine millions of biography pages from Wikipedia and tackle the
generalization problem stemming from the variety and heterogeneity of the
trajectory descriptions. Our ensemble model COSMOS, which combines the idea of
semi-supervised learning and contrastive learning, achieves an F1 score of
85.95%. For this task, we also create a hand-curated dataset,
WikiLifeTrajectory, consisting of 8,852 (person, time, location) triplets as
ground truth. Besides, we perform an empirical analysis on the trajectories of
8,272 historians to demonstrate the validity of the extracted results. To
facilitate the research on trajectory extractions and help the analytical
studies to construct grand narratives, we make our code, the million-level
extracted trajectories, and the WikiLifeTrajectory dataset publicly available.
