Triple
T13166287
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | arrondissement of Thonon-les-Bains |
E312857
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Bernex
Bernex is a small commune in the Haute-Savoie department of southeastern France, known for its Alpine scenery and ski resort.
|
E1023690
|
NE FINISHED |
How this triple was built (4 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: [arrondissement of Thonon-les-Bains, contains, Bernex]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bernex Context triple: [arrondissement of Thonon-les-Bains, contains, Bernex]
-
A.
Bernex
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. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Bernex Triple: [arrondissement of Thonon-les-Bains, contains, Bernex]
Generated description
Bernex is a small commune in the Haute-Savoie department of southeastern France, known for its Alpine scenery and ski resort.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bernex Target entity description: Bernex is a small commune in the Haute-Savoie department of southeastern France, known for its Alpine scenery and ski resort.
-
A.
Bernex
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. chosen
Provenance (5 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_69d806ac3ee081909b2fd27d060aa974 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98c2c317881908cc715c97d915f77 |
completed | April 10, 2026, 11:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6eaf6c9ec8190bc0097d62e57e52a |
completed | May 3, 2026, 6:28 a.m. |
| NEDg | Description generation | batch_69f6ee07aa988190ad8bb3bc3ecf7890 |
completed | May 3, 2026, 6:41 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6eef335b481908dbe6fb93c9d56f2 |
completed | May 3, 2026, 6:45 a.m. |
Created at: April 9, 2026, 9:13 p.m.