Triple
T2497245
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Swiss Reformation |
E52180
|
entity |
| Predicate | hasKeyCity |
P316
|
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: [Swiss Reformation, hasKeyCity, Bern]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bern Context triple: [Swiss Reformation, hasKeyCity, 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.
Canton
Canton is the historical Western name for Guangzhou, a major port city in southern China and the capital of Guangdong province.
-
C.
Canton
Canton is a suburban town in Norfolk County, Massachusetts, located southwest of Boston and known for its residential character and local historic sites.
-
D.
Rochester
Rochester is a small borough in western Pennsylvania situated along the Ohio River in Beaver County.
-
E.
Rochester
Rochester is a major city in western New York State known historically for its role in industry, photography, and social reform movements.
- 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_69ab4955111c8190835bf619adec21ff |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd1ad2f8c81908853e97d75081e84 |
completed | March 7, 2026, 7:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af1f9be594819099a03a2784691124 |
completed | March 9, 2026, 7:29 p.m. |
Created at: March 6, 2026, 9:46 p.m.