Triple
T3768161
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | L’Auto |
E82729
|
entity |
| Predicate | tourDeFranceLeaderJerseyColorInfluence |
P16707
|
FINISHED |
| Object | yellow jersey |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: yellow jersey | Statement: [L’Auto, tourDeFranceLeaderJerseyColorInfluence, yellow jersey]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tourDeFranceLeaderJerseyColorInfluence Context triple: [L’Auto, tourDeFranceLeaderJerseyColorInfluence, yellow jersey]
-
A.
jerseyColorGeneralClassification
chosen
Indicates the color of the jersey worn by the leader of the general classification in a race or competition.
-
B.
TourDeFranceOverallWins
Indicates the number of times an entity has won the overall general classification of the Tour de France.
-
C.
TourDeFranceWins
Indicates the number of times an entity has won the Tour de France cycling race.
-
D.
TourDeFranceWin
Indicates that an entity has won the Tour de France cycling race, typically as the overall general classification winner for a given edition.
-
E.
jerseyColorMountainsClassification
Indicates a classification relationship that assigns or associates a jersey color with a specific mountains-related category or ranking.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ad8b207b0081909d2b48843fbd8795 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcc2bdf6c819088d3c6ace83ca5ea |
completed | March 8, 2026, 7:21 p.m. |
| PD | Predicate disambiguation | batch_69adc04ec36c8190bd5b944d4f4d32aa |
completed | March 8, 2026, 6:30 p.m. |
Created at: March 8, 2026, 3:35 p.m.