Triple
T26735698
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jean-Éric Vergne |
E674102
|
entity |
| Predicate | FormulaOnePoles |
P57362
|
FINISHED |
| Object | 0 |
—
|
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: 0 | Statement: [Jean-Éric Vergne, FormulaOnePoles, 0]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: FormulaOnePoles Context triple: [Jean-Éric Vergne, FormulaOnePoles, 0]
-
A.
achievedPolePositionAt
Indicates that an entity secured the top starting position (pole position) at a specified event or location.
-
B.
polePositions
Indicates that one entity holds the pole position (starting first) relative to another entity in a competitive event, such as a race.
-
C.
grandPrixFastestLaps
Indicates the relationship where a driver records the fastest lap time during a specific Grand Prix race.
-
D.
totalPolePositions
chosen
Indicates the total number of times an entity has achieved pole position in qualifying or starting order across all relevant events.
-
E.
winnerLaps
Indicates that one participant completed more laps than another, thereby winning based on lap count.
- 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_69eecda57ab481909424e98f2835e7d8 |
completed | April 27, 2026, 2:44 a.m. |
| NER | Named-entity recognition | batch_69f6562fd3488190be1acd8c526a28d2 |
completed | May 2, 2026, 7:53 p.m. |
| PD | Predicate disambiguation | batch_69f651a731508190bb0c8c2462eba224 |
completed | May 2, 2026, 7:33 p.m. |
Created at: April 27, 2026, 3:47 a.m.