Triple
T3768162
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | L’Auto |
E82729
|
entity |
| Predicate | tourDeFranceLeaderJerseyColorReason |
P16707
|
FINISHED |
| Object | color of L’Auto’s newsprint |
—
|
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: color of L’Auto’s newsprint | Statement: [L’Auto, tourDeFranceLeaderJerseyColorReason, color of L’Auto’s newsprint]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tourDeFranceLeaderJerseyColorReason Context triple: [L’Auto, tourDeFranceLeaderJerseyColorReason, color of L’Auto’s newsprint]
-
A.
TourDeFranceWins
Indicates the number of times an entity has won the Tour de France cycling race.
-
B.
TourDeFranceWin
Indicates that an entity has won the Tour de France cycling race, typically as the overall general classification winner for a given edition.
-
C.
TourDeFranceOverallWins
Indicates the number of times an entity has won the overall general classification of the Tour de France.
-
D.
jerseyColorGeneralClassification
chosen
Indicates the color of the jersey worn by the leader of the general classification in a race or competition.
-
E.
jerseyColorYoungRiderClassification
Indicates the color of the jersey worn by the leader of the young rider classification in a cycling race.
- 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.