Triple
T26755127
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martin Brundle |
E674648
|
entity |
| Predicate | totalF1Podiums |
P52119
|
FINISHED |
| Object | 9 |
—
|
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: 9 | Statement: [Martin Brundle, totalF1Podiums, 9]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalF1Podiums Context triple: [Martin Brundle, totalF1Podiums, 9]
-
A.
totalFormulaOnePodiums
chosen
Indicates the total number of times an entity has finished on the podium (top three positions) in Formula One races.
-
B.
totalPolePositions
Indicates the total number of times an entity has achieved pole position in qualifying or starting order across all relevant events.
-
C.
achievedFirstF1PodiumWith
Indicates that one entity secured their first-ever Formula 1 podium finish while driving for or in association with the other entity.
-
D.
numberOfF1WorldChampionships
Indicates the number of Formula 1 World Championship titles that an entity has won.
-
E.
F1Points
Indicates that an entity has earned or is associated with a specific number of Formula 1 championship points.
- 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_69eecda6e9dc81908452fab3ba17ed9b |
completed | April 27, 2026, 2:44 a.m. |
| NER | Named-entity recognition | batch_69f64dbbaefc8190952b8320bf4397d8 |
completed | May 2, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_69f64cacd2c08190aed8a1761d0da679 |
completed | May 2, 2026, 7:12 p.m. |
Created at: April 27, 2026, 3:55 a.m.