Triple
T1117495
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Satigny |
E11133
|
entity |
| Predicate | hasNeighbouringMunicipality |
P224
|
FINISHED |
| Object | Dardagny |
E33004
|
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: Dardagny | Statement: [Satigny, hasNeighbouringMunicipality, Dardagny]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dardagny Context triple: [Satigny, hasNeighbouringMunicipality, Dardagny]
-
A.
Dardagny
chosen
Dardagny is a rural Swiss municipality known for its vineyards and scenic landscapes in the western part of the canton of Geneva.
-
B.
Margeride
Margeride is a mountainous and sparsely populated region in south-central France known for its granite plateaus, forests, and traditional rural landscapes.
-
C.
Sauvy
Sauvy is a French surname most notably borne by Alfred Sauvy, a prominent demographer, sociologist, and economist.
-
D.
Marmande
Marmande is a town in southwestern France known for its agricultural production, particularly tomatoes, and its location in the Garonne River valley.
-
E.
Aligoté
Aligoté is a white grape variety from Burgundy known for producing light, crisp, and high-acid wines often enjoyed young.
- 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_69a493252a648190ac48f8742474a5e8 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4bba425a8819099116e479552332e |
completed | March 1, 2026, 10:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac53999b3c8190aff1cf84a3c16909 |
completed | March 7, 2026, 4:34 p.m. |
Created at: March 1, 2026, 7:43 p.m.