Triple
T16460342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Belp |
E399788
|
entity |
| Predicate | hasNeighboringMunicipality |
P224
|
FINISHED |
| Object | Kehrsatz |
E399787
|
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: Kehrsatz | Statement: [Belp, hasNeighboringMunicipality, Kehrsatz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kehrsatz Context triple: [Belp, hasNeighboringMunicipality, Kehrsatz]
-
A.
Kehrsatz
chosen
Kehrsatz is a municipality in the canton of Bern, Switzerland, situated just south of the city of Bern within its metropolitan region.
-
B.
Ehlhalten
Ehlhalten is a village and district of the town of Eppstein in the Rheingau-Taunus region of Hesse, Germany.
-
C.
Tuchlauben
Tuchlauben is a historic street in Vienna’s city center, known for its upscale shops and proximity to major landmarks in the old town.
-
D.
Stößen
Stößen is a small town in the German state of Saxony-Anhalt that forms part of the broader Leipzig metropolitan area.
-
E.
Dettenschwang
Dettenschwang is a village and district of the market town Dießen am Ammersee in the Bavarian region of Germany.
- 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_69d87f2dac988190b74d6e185fa88ba4 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32d80e66c8190b2b3199efe9cfaa1 |
completed | April 18, 2026, 7:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a005817fa088190a0eb85016fe5afc4 |
completed | May 10, 2026, 10:04 a.m. |
Created at: April 10, 2026, 5:10 a.m.