A contrail cannot exist without a flight. We exploit this constraint by inverting the usual detect-then-attribute pipeline: instead of finding contrails in an image and asking which flight made them, we start from each flight and ask whether its contrail is visible. Aircraft positions from surveillance data and reanalysis wind fields feed a contrail lifecycle model that predicts where each flight's contrail should appear in a ground-based camera image, not at the aircraft's position but where the wind has carried the plume. These predictions become spatial prompts that guide a video segmentation model to either outline the contrail or reject the prompt when nothing is visible. Because each prompt belongs to exactly one flight, the output mask carries the flight's identity by construction, and tracking across frames requires no re-identification. We compare prompt encodings of increasing physical richness: binary presence, age-weighted freshness, and suppression of competing flights. On the Ground Visible Camera Contrail Sequences dataset, the richest design improves mean average precision, averaged over intersection-over-union matching thresholds from 0.25 to 0.75, by 25 % over the binary baseline, reaches 96.2 % attribution precision, rejects empty prompts with an area under the receiver operating characteristic curve of 0.97, and maintains detection across roughly three-quarters of a contrail's visible lifetime. A video-level cluster bootstrap confirms the attribution and segmentation-quality gains, and is equally clear that the richer prompts do <em>not</em> find more contrails: detection coverage is statistically indistinguishable from the binary baseline. What richer prompts buy is delineation and identity, not discovery.