Triple
T7502685
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frantz Reichel |
E177303
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | L’Auto |
E82729
|
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: L’Auto | Statement: [Frantz Reichel, employer, L’Auto]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: L’Auto Context triple: [Frantz Reichel, employer, L’Auto]
-
A.
L’Auto
chosen
L’Auto was a French sports newspaper best known for creating and organizing the Tour de France.
-
B.
La Coche
La Coche is one of the small islands in the Les Saintes archipelago in the Caribbean, known for its rugged coastline and surrounding marine life.
-
C.
The Rambler
The Rambler is Samuel Johnson’s influential 18th-century periodical of moral and philosophical essays that helped establish his reputation as a leading English man of letters.
-
D.
Renault Vel Satis
The Renault Vel Satis is a large, unconventional French executive hatchback produced in the early 2000s, known for its distinctive styling and emphasis on comfort and technology.
-
E.
Bilen
Bilen is a Cushitic language spoken primarily by the Bilen people in central Eritrea.
- 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_69c69f2696688190915a8458f2398211 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f59be2748190ad8e94179f594e51 |
completed | March 27, 2026, 9:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c83c9953e88190a1e0e899f2ddf822 |
completed | March 28, 2026, 8:39 p.m. |
Created at: March 27, 2026, 3:44 p.m.