Triple
T13013974
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bernese Mittelland |
E322495
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Burgdorf |
E401058
|
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: Burgdorf | Statement: [Bernese Mittelland, containsCity, Burgdorf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Burgdorf Context triple: [Bernese Mittelland, containsCity, Burgdorf]
-
A.
Burgdorf
chosen
Burgdorf is a historic Swiss town in the canton of Bern, known for its medieval castle and role as a regional economic and cultural center.
-
B.
Bremgarten
Bremgarten is a historic Swiss town in the canton of Aargau, known for its well-preserved medieval old town and scenic riverside setting.
-
C.
Aarburg
Aarburg is a historic Swiss town in the canton of Aargau, known for its prominent riverside fortress overlooking the Aare River.
-
D.
Bönigen
Bönigen is a Swiss village in the canton of Bern, known for its scenic location on the shore of Lake Brienz near Interlaken.
-
E.
Hergiswil
Hergiswil is a Swiss lakeside municipality known for its scenic setting on Lake Lucerne and its historic glassworks.
- 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_69d807657e8c8190bd9435ee2f823845 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97ecbb8f4819094d55eb07cb5ad97 |
completed | April 10, 2026, 10:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f716b885708190b6c38c481fa9ca21 |
completed | May 3, 2026, 9:34 a.m. |
Created at: April 9, 2026, 8:50 p.m.