Triple
T36045631
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charles Leclerc |
E1042662
|
entity |
| Predicate | racesWithLicence |
P19269
|
FINISHED |
| Object | Monégasque racing licence |
—
|
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: Monégasque racing licence | Statement: [Charles Leclerc, racesWithLicence, Monégasque racing licence]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: racesWithLicence Context triple: [Charles Leclerc, racesWithLicence, Monégasque racing licence]
-
A.
racedWith
Indicates that one entity participated in a race or competitive speed event together with another entity.
-
B.
racesAgainst
Indicates that one entity competes in a race directly against another entity.
-
C.
typeOfLicense
chosen
Indicates the specific kind or category of license associated with an entity.
-
D.
hasRacingDiscipline
Indicates that an entity participates in, is associated with, or is characterized by a specific type or category of racing discipline.
-
E.
racedAs
Indicates that one entity participated in a race or competition under the identity, name, or classification of another entity.
- 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_69f76e2e41f8819091f9fb0536920fec |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7b2c771108190adeec151daad5dab |
completed | May 3, 2026, 8:40 p.m. |
| PD | Predicate disambiguation | batch_69f7b1bad2e88190963ab4ee5d4f2038 |
completed | May 3, 2026, 8:36 p.m. |
Created at: May 3, 2026, 4:07 p.m.