Triple
T13339863
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Israeli ambassador to Switzerland |
E317795
|
entity |
| Predicate | seat |
P75
|
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: [Israeli ambassador to Switzerland, seat, Bern]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bern Context triple: [Israeli ambassador to Switzerland, seat, 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 British actress and writer known for her work in film, television, and radio since the 1960s.
-
C.
Bron
Bron is a suburban commune in eastern France that forms part of the metropolitan area of Lyon.
-
D.
Canton
Canton is the historical Western name for Guangzhou, a major port city in southern China and the capital of Guangdong province.
-
E.
Canton
Canton is a historic waterfront neighborhood in southeast Baltimore, Maryland, known for its revitalized harborfront, rowhouses, and vibrant bar and restaurant scene.
- 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_69d806b5a3c08190b42c267fb092f98a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99d01bf8481908cd3a99e5557b972 |
completed | April 11, 2026, 12:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f71f3ecf4c8190bb9eee699859dc08 |
completed | May 3, 2026, 10:11 a.m. |
Created at: April 9, 2026, 9:31 p.m.