Triple
T8582306
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prince Sébastien of Luxembourg |
E203212
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Sébastien |
E278395
|
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: Sébastien | Statement: [Prince Sébastien of Luxembourg, givenName, Sébastien]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sébastien Context triple: [Prince Sébastien of Luxembourg, givenName, Sébastien]
-
A.
Sébastien
chosen
Sébastien is the French form of the given name Sebastian, commonly used in French-speaking countries.
-
B.
Hector LeMans
Hector LeMans is the primary antagonist of the adventure game Grim Fandango, a corrupt crime boss in the Land of the Dead who orchestrates a large-scale ticket fraud scheme.
-
C.
Sébastien David
Sébastien David is a French local politician serving as the mayor of the commune of Saint-Affrique in southern France.
-
D.
Arnaud
Arnaud is a small commune located in Haiti’s Nippes Department.
-
E.
Clément
Clément is a French given name, equivalent to Clement in English, commonly used for males.
- 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_69ca8329bb7c8190a63c643730839103 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbeb1bbbd8819082670286a711826d |
completed | March 31, 2026, 3:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cea89e4658819090cc6e94e934670b |
completed | April 2, 2026, 5:34 p.m. |
Created at: March 30, 2026, 6:22 p.m.