Triple
T16294478
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sébastien Loeb |
E395611
|
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: [Sébastien Loeb, givenName, Sébastien]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sébastien Context triple: [Sébastien Loeb, 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.
Julien Carette
Julien Carette was a French character actor known for his comic roles in classic French cinema of the 1930s and 1940s.
-
E.
Arnaud
Arnaud is a small commune located in Haiti’s Nippes Department.
- 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_69d87f22c7248190a54c949738441e2e |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e25e2c255881909d99c43770475329 |
completed | April 17, 2026, 4:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a001f9965b8819080278ccef15288aa |
completed | May 10, 2026, 6:03 a.m. |
Created at: April 10, 2026, 5:05 a.m.