Triple
T3336718
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | German-speaking Switzerland |
E70155
|
entity |
| Predicate | includesCanton |
P5501
|
FINISHED |
| Object | Bern |
E18380
|
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: Bern | Statement: [German-speaking Switzerland, includesCanton, Bern]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bern Context triple: [German-speaking Switzerland, includesCanton, Bern]
-
A.
Bern
chosen
Bern is the capital city of Switzerland, known for its well-preserved medieval old town and role as a political and cultural center.
-
B.
Bron
Bron is a suburban commune in eastern France that forms part of the metropolitan area of Lyon.
-
C.
Canton
Canton is the historical Western name for Guangzhou, a major port city in southern China and the capital of Guangdong province.
-
D.
Canton
Canton is a suburban town in Norfolk County, Massachusetts, located southwest of Boston and known for its residential character and local historic sites.
-
E.
Canton
Canton is a small New England town in Hartford County, Connecticut, known for its historic village centers and scenic Farmington River setting.
- 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_69ad85a24f208190bcf83131bfed3521 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb1bad97481909359e914d44a1a74 |
completed | March 8, 2026, 5:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b325191a38819095cdccca8f013774 |
completed | March 12, 2026, 8:42 p.m. |
Created at: March 8, 2026, 3:12 p.m.