Triple
T21428757
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ford GT40 |
E528627
|
entity |
| Predicate | LeMansWinStreak |
P143932
|
FINISHED |
| Object | four consecutive victories |
—
|
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: four consecutive victories | Statement: [Ford GT40, LeMansWinStreak, four consecutive victories]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: LeMansWinStreak Context triple: [Ford GT40, LeMansWinStreak, four consecutive victories]
-
A.
won24HoursOfLeMansInYear
Indicates that the subject achieved victory in the 24 Hours of Le Mans race in the specified year.
-
B.
winnerLaps
Indicates that one participant completed more laps than another, thereby winning based on lap count.
-
C.
worldDriversChampionCarNumber
Indicates the car number driven by the Formula 1 World Drivers' Champion in a given season.
-
D.
grandPrixWin
Indicates that an entity has achieved victory in a Grand Prix event or race.
-
E.
grandPrixFastestLaps
Indicates the relationship where a driver records the fastest lap time during a specific Grand Prix 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_69e0c455f3688190810bc96365791b0f |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ee813db52c8190ac933bc6ec4dbf77 |
completed | April 26, 2026, 9:18 p.m. |
| PD | Predicate disambiguation | batch_69e61639ee288190889ffd500d1260f6 |
completed | April 20, 2026, 12:04 p.m. |
| PDg | Predicate description generation | batch_69e6190163448190a2404b396215c686 |
completed | April 20, 2026, 12:16 p.m. |
Created at: April 16, 2026, 5:49 p.m.