Triple
T28991081
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2022 São Paulo Grand Prix qualifying |
E736026
|
entity |
| Predicate | polePositionLapNumber |
P166275
|
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: [2022 São Paulo Grand Prix qualifying, polePositionLapNumber, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: polePositionLapNumber Context triple: [2022 São Paulo Grand Prix qualifying, polePositionLapNumber, 1]
-
A.
positionOnLap
Indicates that one entity is physically situated resting on the lap area of another entity.
-
B.
polePositionDriverTeam
Indicates which team the driver who secured pole position in a race was driving for.
-
C.
polePositionCarNumber
Indicates which car number achieved the pole position in a race.
-
D.
polePositions
Indicates that one entity holds the pole position (starting first) relative to another entity in a competitive event, such as a race.
-
E.
polePositionDriverNationality
Indicates the nationality associated with the driver who secured pole position in a 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_69f077eacd0481908ef0bafd74491cd0 |
completed | April 28, 2026, 9:03 a.m. |
| NER | Named-entity recognition | batch_69f66003a3f48190a2ba6da5aafbb5cb |
completed | May 2, 2026, 8:35 p.m. |
| PD | Predicate disambiguation | batch_69f65c2198208190a3954086c22cfcbf |
completed | May 2, 2026, 8:18 p.m. |
| PDg | Predicate description generation | batch_69f65f75ac608190a62cd6afce14f68e |
completed | May 2, 2026, 8:32 p.m. |
Created at: April 28, 2026, 9:25 a.m.