Triple
T6566855
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gaston Chevrolet |
E153928
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Gaston |
E231330
|
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: Gaston | Statement: [Gaston Chevrolet, givenName, Gaston]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gaston Context triple: [Gaston Chevrolet, givenName, Gaston]
-
A.
Gaston
chosen
Gaston is a masculine given name of French origin commonly used in Francophone countries and beyond.
-
B.
Gaspard
Gaspard is a French masculine given name historically borne by notable figures such as nobles, military leaders, and artists.
-
C.
Greuze
Greuze is a French surname most famously associated with Jean-Baptiste Greuze, an 18th-century painter known for his sentimental and moralizing genre scenes.
-
D.
Luc Oursel
Luc Oursel was a French business executive best known for leading the nuclear energy company Areva during the early 2010s.
-
E.
Douzy
Douzy is a small commune in the Ardennes department of northern France, known for its rural character and cross-border ties, including a town twinning with Kaiserslautern in Germany.
- 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_69c6880cb35881909b763eb0125236b9 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6ae5381e88190b44dc4440efdd8ae |
completed | March 27, 2026, 4:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6d564cb908190bb8885e6c8d8abac |
completed | March 27, 2026, 7:07 p.m. |
Created at: March 27, 2026, 1:53 p.m.