Triple
T14549470
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grilly |
E341375
|
entity |
| Predicate | hasBorder |
P224
|
FINISHED |
| Object | Sauverny |
E341374
|
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: Sauverny | Statement: [Grilly, hasBorder, Sauverny]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sauverny Context triple: [Grilly, hasBorder, Sauverny]
-
A.
Sauverny
chosen
Sauverny is a small commune in eastern France’s Ain department, near the Swiss border and the Geneva metropolitan area.
-
B.
Souvigny
Souvigny is a historic town in central France known for its important Cluniac priory and medieval religious heritage.
-
C.
Yerville
Yerville is a small commune in the Seine-Maritime department of the Normandy region in northern France.
-
D.
Chauvigny
Chauvigny is a historic town in western France known for its medieval fortifications and picturesque setting in the Vienne department of the Nouvelle-Aquitaine region.
-
E.
Vallentigny
Vallentigny is a small French commune located in the Aube department in the Grand Est region of north-central France.
- 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_69d822db9c8481908213ceb39585f792 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb2ed2b4c8190945bd26531c71f1f |
completed | April 14, 2026, 9:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd7a6344b08190a3c1124c6dd7da96 |
completed | May 8, 2026, 5:53 a.m. |
Created at: April 10, 2026, 1:23 a.m.