Triple
T13543042
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kyalami Grand Prix Circuit |
E323438
|
entity |
| Predicate | firstUsedForFormulaOne |
P102108
|
FINISHED |
| Object | 1967 |
—
|
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: 1967 | Statement: [Kyalami Grand Prix Circuit, firstUsedForFormulaOne, 1967]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstUsedForFormulaOne Context triple: [Kyalami Grand Prix Circuit, firstUsedForFormulaOne, 1967]
-
A.
firstFormulaOneWin
Indicates that the subject achieved their first victory in a Formula One race in relation to the specified event or context.
-
B.
ageAtFirstFormulaOneWin
Indicates the age a person was when they achieved their first Formula One race victory.
-
C.
enteredFormulaOne
Indicates that an entity began competing in Formula One racing, marking its entry into the Formula One championship.
-
D.
totalFormulaOneStarts
Indicates the total number of times an entity has started in Formula One races.
-
E.
firstF1GrandPrixYear
chosen
Indicates the year in which a given Formula 1 Grand Prix was first held.
- 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_69d8076776248190bdf0d4fa1f85a5fc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbbb9ee3f081909056dc1a92c40b7a |
completed | April 12, 2026, 3:34 p.m. |
| PD | Predicate disambiguation | batch_69dbae13bec4819084c1770638c00ed9 |
completed | April 12, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:45 p.m.