Triple
T4046833
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gardon de Saint-Jean |
E84085
|
entity |
| Predicate | tributaryOf |
P415
|
FINISHED |
| Object | Gardon |
E317805
|
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: Gardon | Statement: [Gardon de Saint-Jean, tributaryOf, Gardon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gardon Context triple: [Gardon de Saint-Jean, tributaryOf, Gardon]
-
A.
Gardon
chosen
The Gardon is a river in southern France known for flowing through the Gard department and beneath the famous Pont du Gard Roman aqueduct.
-
B.
Sauvestre
Sauvestre is a French surname most notably associated with architect Stephen Sauvestre, who contributed to the design of the Eiffel Tower.
-
C.
Doncieux
Doncieux is a French surname most notably associated with Camille Doncieux, the first wife and frequent model of painter Claude Monet.
-
D.
Nantz
Nantz is the surname of Jim Nantz, a prominent American sportscaster best known for his long-running work with CBS Sports covering events like the NFL, NCAA basketball, and The Masters.
-
E.
Dugommier
Dugommier was a French Revolutionary general noted for his leadership in key campaigns such as the Siege of Toulon and the War of the Pyrenees.
- 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_69aed930bd5c819083e7dcc14fc44f69 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefb62593c8190ab8462c4d9cd9d08 |
completed | March 9, 2026, 4:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5629c312c8190b89af732e2ad0fa3 |
completed | March 14, 2026, 1:29 p.m. |
Created at: March 9, 2026, 3:37 p.m.