Triple
T18642159
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | World Champions Centre |
E455714
|
entity |
| Predicate | hasNotableCoach |
P550
|
FINISHED |
| Object | Laurent Landi |
—
|
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: Laurent Landi | Statement: [World Champions Centre, hasNotableCoach, Laurent Landi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laurent Landi Context triple: [World Champions Centre, hasNotableCoach, Laurent Landi]
-
A.
Laurent Landi
chosen
Laurent Landi is an elite gymnastics coach best known for coaching Olympic champion Simone Biles.
-
B.
Laurent Lomet
Laurent Lomet was a mountaineer known for participating in the first recorded ascent of Monte Perdido in the Pyrenees.
-
C.
Laurent Vastel
Laurent Vastel is a French local politician who serves as the mayor of the Paris suburb Fontenay-aux-Roses.
-
D.
Laurent Chalumeau
Laurent Chalumeau is a French writer and screenwriter known for his work in film and literature.
-
E.
Laurent Lucas
Laurent Lucas is a French actor known for his intense performances in psychological dramas and thrillers, including prominent roles in contemporary French cinema.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d38ea1e88190997e9b231190ba6f |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e54fcd6da081908030b052727f2c2f |
completed | April 19, 2026, 9:57 p.m. |
Created at: April 10, 2026, 11:47 a.m.