Triple
T36045636
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charles Leclerc |
E1042662
|
entity |
| Predicate | F1GrandPrixVictories |
P52118
|
FINISHED |
| Object | multiple Formula One race wins |
—
|
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: multiple Formula One race wins | Statement: [Charles Leclerc, F1GrandPrixVictories, multiple Formula One race wins]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: F1GrandPrixVictories Context triple: [Charles Leclerc, F1GrandPrixVictories, multiple Formula One race wins]
-
A.
numberOfF1WorldChampionships
Indicates the number of Formula 1 World Championship titles that an entity has won.
-
B.
grandPrixRaceWins
Indicates the number of Grand Prix races that an entity has won.
-
C.
totalFormulaOneWins
chosen
Indicates the total number of Formula One race victories achieved by a given driver, team, or other relevant entity.
-
D.
grandPrixWinRate
Indicates the proportion of Grand Prix events that an entity has won relative to the total number of Grand Prix events it has participated in.
-
E.
grandPrixWin
Indicates that an entity has achieved victory in a Grand Prix event or 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_69f76e2e41f8819091f9fb0536920fec |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7b2c771108190adeec151daad5dab |
completed | May 3, 2026, 8:40 p.m. |
| PD | Predicate disambiguation | batch_69f7b1bad2e88190963ab4ee5d4f2038 |
completed | May 3, 2026, 8:36 p.m. |
Created at: May 3, 2026, 4:07 p.m.