Triple
T735834
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vetsuisse Faculty |
E14929
|
entity |
| Predicate | location |
P40
|
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: [Vetsuisse Faculty, location, Bern]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bern Context triple: [Vetsuisse Faculty, location, 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.
Rochester
Rochester is a major city in western New York State known historically for its role in industry, photography, and social reform movements.
-
D.
Burlington
Burlington is a historic city in present-day New Jersey that once served as the colonial capital of the Province of New Jersey.
-
E.
Burlington
Burlington is a suburban town in Massachusetts known for its proximity to Boston and its mix of residential neighborhoods, office parks, and retail centers.
- 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_69a4934d9930819099eed80096b0597d |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a5da30b88190afbd12ae6109cc1b |
completed | March 1, 2026, 8:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7c00751dc8190ad1ac6885341f799 |
completed | March 4, 2026, 5:15 a.m. |
Created at: March 1, 2026, 7:37 p.m.