Triple
T18500973
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seeland (Bernese Lakeland) |
E452063
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object | Aarberg |
—
|
NE NERFINISHED |
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: Aarberg | Statement: [Seeland (Bernese Lakeland), containsTown, Aarberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aarberg Context triple: [Seeland (Bernese Lakeland), containsTown, Aarberg]
-
A.
Aarberg
chosen
Aarberg is a small historic town in the canton of Bern in Switzerland, known for its medieval center and distinctive wooden bridge over the Aare River.
-
B.
Bettlach
Bettlach is a Swiss municipality located in the canton of Solothurn.
-
C.
Aarburg
Aarburg is a historic Swiss town in the canton of Aargau, known for its prominent riverside fortress overlooking the Aare River.
-
D.
Zugerberg
Zugerberg is a scenic mountain and recreational area in the Swiss canton of Zug, known for its panoramic views over Lake Zug and the surrounding Alps.
-
E.
Oberegg
Oberegg is a Swiss municipality in the canton of Appenzell Innerrhoden, known for its rural landscape and location in the Appenzell region.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d3855d50819097fc8561b0299dd9 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e532c43de48190b49b87c1bb591016 |
completed | April 19, 2026, 7:53 p.m. |
Created at: April 10, 2026, 11:36 a.m.