Triple
T3748692
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bern |
E81271
|
entity |
| Predicate | demonym |
P191
|
FINISHED |
| Object | Berner |
E117823
|
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: Berner | Statement: [Bern, demonym, Berner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Berner Context triple: [Bern, demonym, Berner]
-
A.
Berner
chosen
A Berner is a resident or native of the Swiss city of Bern.
-
B.
Murten
Murten is a historic bilingual town in the canton of Fribourg, Switzerland, known for its well-preserved medieval old town and lakeside setting on Lake Murten.
-
C.
Bernex
Bernex is a municipality in western Switzerland located near the city of Geneva, known for its semi-rural character and surrounding vineyards.
-
D.
Solothurn
Solothurn is a canton in northwestern Switzerland known for its historic baroque town of the same name and its location along the Aare River.
-
E.
Grenchen
Grenchen is a Swiss town in the canton of Solothurn known for its watchmaking industry and location at the foot of the Jura Mountains.
- 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_69ad8b19b7b08190a6188804e99c53e9 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcb6bf95c81909796fbc84995ae05 |
completed | March 8, 2026, 7:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4db2f5e9881908c10feafbb569f48 |
completed | March 14, 2026, 3:51 a.m. |
Created at: March 8, 2026, 3:35 p.m.