Triple
T21392008
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luigi Ganna |
E527676
|
entity |
| Predicate | hasNumberOfGiroOverallWins |
P17172
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [Luigi Ganna, hasNumberOfGiroOverallWins, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfGiroOverallWins Context triple: [Luigi Ganna, hasNumberOfGiroOverallWins, 1]
-
A.
numberOfGrandTourOverallVictories
chosen
Indicates the total count of times an entity has won the overall classification in any of cycling’s Grand Tours (Tour de France, Giro d’Italia, or Vuelta a España).
-
B.
numberOfGiroDiLombardiaWins
Indicates the number of times an entity has won the Giro di Lombardia cycling race.
-
C.
hasGrandTourStageWins
Indicates that one entity has achieved victories in stages of a Grand Tour cycling race (Tour de France, Giro d’Italia, or Vuelta a España).
-
D.
numberOfVueltaAEspanaOverallVictories
Indicates the total count of times an entity has won the Vuelta a España general (overall) classification.
-
E.
hasWonAllGrandTours
Indicates that the subject has won all three cycling Grand Tours (Tour de France, Giro d'Italia, and Vuelta a España) at least once.
- 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_69e0b51ff3748190935c0a513c62a12b |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69ee62cbfef08190a33ac1f198c82cd0 |
completed | April 26, 2026, 7:09 p.m. |
| PD | Predicate disambiguation | batch_69e6162bbfc88190a3e75859941b2638 |
completed | April 20, 2026, 12:03 p.m. |
Created at: April 16, 2026, 5:13 p.m.