Triple
T12594161
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Troinex |
E300690
|
entity |
| Predicate | hasNeighboringMunicipality |
P224
|
FINISHED |
| Object | Carouge |
E68492
|
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: Carouge | Statement: [Troinex, hasNeighboringMunicipality, Carouge]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Carouge Context triple: [Troinex, hasNeighboringMunicipality, Carouge]
-
A.
Carouge
chosen
Carouge is a municipality in southwestern Switzerland known for its historic Sardinian-style old town and vibrant cultural scene, located just south of the city of Geneva.
-
B.
Yverdon-les-Bains
Yverdon-les-Bains is a historic spa town in western Switzerland known for its thermal baths, medieval castle, and location at the southern end of Lake Neuchâtel.
-
C.
Neuchâtel
Neuchâtel is a French-speaking canton in western Switzerland known for its lakeside capital, watchmaking industry, and historic architecture.
-
D.
Aubonne
Aubonne is a river in western Switzerland that flows through the canton of Vaud before emptying into Lake Geneva.
-
E.
Lausanne
Lausanne is a major Swiss city on the shores of Lake Geneva, known for hosting the International Olympic Committee and its vibrant cultural and academic institutions.
- 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_69d7bdea2ca881908f379526c13b1145 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d954cde3c0819094e74413d6dcf548 |
completed | April 10, 2026, 7:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f77f70928c8190a872ecb47b8da2c7 |
completed | May 3, 2026, 5:01 p.m. |
Created at: April 9, 2026, 5:08 p.m.