Triple
T26735690
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jean-Éric Vergne |
E674102
|
entity |
| Predicate | FormulaOneTeams |
P19695
|
FINISHED |
| Object | Scuderia Toro Rosso |
—
|
NE NERFINISHED |
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: Scuderia Toro Rosso | Statement: [Jean-Éric Vergne, FormulaOneTeams, Scuderia Toro Rosso]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: FormulaOneTeams Context triple: [Jean-Éric Vergne, FormulaOneTeams, Scuderia Toro Rosso]
-
A.
racingTeam
Indicates that one entity is a racing team associated with, employing, or represented by the other entity in a competitive racing context.
-
B.
featuresTeams
Indicates that something prominently presents or includes specific teams as a central element or focus.
-
C.
firstFormulaOneTeam
Indicates the Formula One team for which an entity (typically a driver) first competed.
-
D.
fieldsTeam
chosen
Indicates that an entity is a member of, or associated as part of, a particular team.
-
E.
lastF1Team
Indicates the Formula 1 team for which an entity most recently competed or was last associated.
- 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_69eecda57ab481909424e98f2835e7d8 |
completed | April 27, 2026, 2:44 a.m. |
| NER | Named-entity recognition | batch_69f6383625cc8190aa223d8ef655743c |
completed | May 2, 2026, 5:45 p.m. |
| PD | Predicate disambiguation | batch_69f63709e4848190b5cf322e06b23fb6 |
completed | May 2, 2026, 5:40 p.m. |
Created at: April 27, 2026, 3:47 a.m.