Triple
T23448475
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thierry Hermès |
E565608
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Thierry |
—
|
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: Thierry | Statement: [Thierry Hermès, givenName, Thierry]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thierry Context triple: [Thierry Hermès, givenName, Thierry]
-
A.
Thierry
chosen
Thierry is a French given name most famously borne by legendary footballer Thierry Henry.
-
B.
Thiéry
Thiéry is a small rural commune in the Alpes-Maritimes department of southeastern France, situated in the mountainous hinterland above Nice.
-
C.
Thibaut
Thibaut is the surrealist resistance fighter protagonist navigating an alternate-history, demon-infested Paris in China Miéville’s novel *The Last Days of New Paris*.
-
D.
Didier
Didier is a masculine given name of French origin, notably borne by Ivorian football legend Didier Drogba.
-
E.
Didier
Didier is a French comedy film written, directed by, and starring Alain Chabat, in which a man is unexpectedly transformed into a dog-like human.
- 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_69e24584f9488190bb32730bd2ce023e |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1a64b27988190b4722425da964407 |
completed | April 29, 2026, 6:33 a.m. |
Created at: April 17, 2026, 5:52 p.m.