Triple
T24365004
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jules Bianchi |
E614169
|
entity |
| Predicate | F1PointsScored |
P103570
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Jules Bianchi, F1PointsScored, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: F1PointsScored Context triple: [Jules Bianchi, F1PointsScored, 2]
-
A.
F1Points
chosen
Indicates that an entity has earned or is associated with a specific number of Formula 1 championship points.
-
B.
totalFormulaOnePodiums
Indicates the total number of times an entity has finished on the podium (top three positions) in Formula One races.
-
C.
bestFormulaOneChampionshipPosition
Indicates the highest (best) finishing position an entity has ever achieved in a Formula One World Championship season.
-
D.
topScorerPoints
Indicates the number of points scored by the top-scoring entity in a given context or event.
-
E.
totalPointsScored
Indicates the total number of points accumulated or scored by an entity over a defined period, event, or context.
- 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_69e2d7dfe7f08190b7a1f3a36483ab05 |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f293874f7c8190b472e99640e97f62 |
completed | April 29, 2026, 11:25 p.m. |
| PD | Predicate disambiguation | batch_69f287bb1b2c81909c2e7fcc392ad143 |
completed | April 29, 2026, 10:35 p.m. |
Created at: April 18, 2026, 2:01 a.m.