Triple
T5876699
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Geneva wine region |
E130642
|
entity |
| Predicate | includesCommune |
P15149
|
FINISHED |
| Object | Bernex |
E53379
|
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: Bernex | Statement: [Geneva wine region, includesCommune, Bernex]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bernex Context triple: [Geneva wine region, includesCommune, Bernex]
-
A.
Bernex
chosen
Bernex is a municipality in western Switzerland located near the city of Geneva, known for its semi-rural character and surrounding vineyards.
-
B.
Berner
A Berner is a resident or native of the Swiss city of Bern.
-
C.
Murten
Murten is a historic bilingual town in the canton of Fribourg, Switzerland, known for its well-preserved medieval old town and lakeside setting on Lake Murten.
-
D.
Arlon
Arlon is a historic town in southeastern Belgium that serves as the capital of the province of Luxembourg in the Walloon Region.
-
E.
Cologny
Cologny is an affluent municipality on the shores of Lake Geneva in Switzerland, known for its scenic views and as the home of the World Economic Forum’s headquarters.
- 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_69c0085523688190bfd487479ce819e6 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0362fb6948190bdbb3f1d446d070c |
completed | March 22, 2026, 6:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0b12861c081909f95f1ef6a1f457c |
completed | March 23, 2026, 3:19 a.m. |
Created at: March 22, 2026, 3:57 p.m.