Triple
T14145606
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Raymond Poulidor |
E350539
|
entity |
| Predicate | numberOfTourDeFranceStarts |
P112991
|
FINISHED |
| Object | 14 |
—
|
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: 14 | Statement: [Raymond Poulidor, numberOfTourDeFranceStarts, 14]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTourDeFranceStarts Context triple: [Raymond Poulidor, numberOfTourDeFranceStarts, 14]
-
A.
firstTourDeFranceYear
Indicates the year in which an entity (typically a cyclist) first participated in the Tour de France.
-
B.
firstTourDeFranceFinishYear
Indicates the year in which an entity (typically a cyclist) achieved their first finish in the Tour de France.
-
C.
firstTourDeFranceHeld
Indicates that the subject is the location or context in which the first Tour de France event was held.
-
D.
TourDeFranceOverallWins
Indicates the number of times an entity has won the overall general classification of the Tour de France.
-
E.
TourDeFranceWins
Indicates the number of times an entity has won the Tour de France cycling race.
- F. None of above. chosen
Provenance (4 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_69d827865f608190b311820428ae027b |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de612266248190a8591b646fe30ae6 |
completed | April 14, 2026, 3:45 p.m. |
| PD | Predicate disambiguation | batch_69de05b5e7a08190a16be9ad8b92b80c |
completed | April 14, 2026, 9:15 a.m. |
| PDg | Predicate description generation | batch_69de239a02e881909b0e2679487e4ab2 |
completed | April 14, 2026, 11:23 a.m. |
Created at: April 10, 2026, 12:53 a.m.