Triple
T7070419
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catherine Thierry |
E164674
|
entity |
| Predicate | hasFamilyName |
P18
|
FINISHED |
| Object | Thierry |
E171385
|
NE 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: Thierry | Statement: [Catherine Thierry, hasFamilyName, Thierry]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thierry Context triple: [Catherine Thierry, hasFamilyName, 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.
Didier
Didier is a masculine given name of French origin, notably borne by Ivorian football legend Didier Drogba.
-
D.
Thierry Noir
Thierry Noir is a French artist renowned for being one of the first to paint large, colorful murals on the Berlin Wall, helping transform it into a symbol of artistic and political expression.
-
E.
Thibault
Thibault is a surname most notably associated with Mike Thibault, a prominent American basketball coach in the WNBA.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c6887b96548190a8a9b3ac8adf4119 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e4c862f481908d1faf6ed57774f1 |
completed | March 27, 2026, 8:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7945a98108190b982ad41222333e4 |
completed | March 28, 2026, 8:42 a.m. |
Created at: March 27, 2026, 2:39 p.m.