Triple
T35751768
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1950 Swiss Grand Prix |
E1033336
|
entity |
| Predicate | hadPointsForFastestLap |
P109349
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [1950 Swiss Grand Prix, hadPointsForFastestLap, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadPointsForFastestLap Context triple: [1950 Swiss Grand Prix, hadPointsForFastestLap, true]
-
A.
fastestLapBonusPoints
chosen
Indicates that bonus points are awarded to an entity for achieving the fastest lap in a given event or context.
-
B.
fastestLapShared
Indicates that two or more participants share the same fastest lap time in a given event or session.
-
C.
fastestLapDriverCountry
Indicates the country associated with the driver who recorded the fastest lap in a given race or session.
-
D.
fastestLapTime
Indicates the shortest recorded time an entity achieved to complete a single lap in a given context or event.
-
E.
fastestLapLapNumber
Indicates the specific lap number on which the fastest lap was achieved in a 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_69f76e1262f48190a313318665acc189 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7a197aee48190bbd69f670a3f7721 |
completed | May 3, 2026, 7:27 p.m. |
| PD | Predicate disambiguation | batch_69f7a070e23881909a233370acb57384 |
completed | May 3, 2026, 7:22 p.m. |
Created at: May 3, 2026, 4:06 p.m.