Triple
T6331869
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Niederbipp |
E142398
|
entity |
| Predicate | hasNeighboringMunicipality |
P224
|
FINISHED |
| Object |
Wolfisberg
Wolfisberg is a small Swiss municipality in the canton of Bern, known for its rural setting in the Oberaargau region.
|
E585770
|
NE FINISHED |
How this triple was built (4 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: Wolfisberg | Statement: [Niederbipp, hasNeighboringMunicipality, Wolfisberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wolfisberg Context triple: [Niederbipp, hasNeighboringMunicipality, Wolfisberg]
-
A.
Wildberg
Wildberg is a small town in the Black Forest region of Baden-Württemberg, Germany, known for its scenic setting along the Nagold River and historic half-timbered architecture.
-
B.
Wolfwil
Wolfwil is a small Swiss municipality in the canton of Solothurn, known for its rural character and location in the Gäu region.
-
C.
Nottwil
Nottwil is a Swiss municipality in the canton of Lucerne, known for its lakeside location and the Swiss Paraplegic Centre.
-
D.
Marienthal
Marienthal is a district of the German city of Zwickau, known primarily as a residential area with local amenities and green spaces.
-
E.
Hornberg
Hornberg is a small town in the Black Forest region of Baden-Württemberg, Germany, known for its scenic landscape and traditional cuckoo clock craftsmanship.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Wolfisberg Triple: [Niederbipp, hasNeighboringMunicipality, Wolfisberg]
Generated description
Wolfisberg is a small Swiss municipality in the canton of Bern, known for its rural setting in the Oberaargau region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Wolfisberg Target entity description: Wolfisberg is a small Swiss municipality in the canton of Bern, known for its rural setting in the Oberaargau region.
-
A.
Wildberg
Wildberg is a small town in the Black Forest region of Baden-Württemberg, Germany, known for its scenic setting along the Nagold River and historic half-timbered architecture.
-
B.
Wolfwil
Wolfwil is a small Swiss municipality in the canton of Solothurn, known for its rural character and location in the Gäu region.
-
C.
Nottwil
Nottwil is a Swiss municipality in the canton of Lucerne, known for its lakeside location and the Swiss Paraplegic Centre.
-
D.
Marienthal
Marienthal is a district of the German city of Zwickau, known primarily as a residential area with local amenities and green spaces.
-
E.
Hornberg
Hornberg is a small town in the Black Forest region of Baden-Württemberg, Germany, known for its scenic landscape and traditional cuckoo clock craftsmanship.
- F. None of above. chosen
Provenance (5 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_69c008d4d8e88190ad301c05b08722ac |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0651634b08190b54860ba0a70f5c4 |
completed | March 22, 2026, 9:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6041f713c8190b27ba54181049377 |
completed | March 27, 2026, 4:14 a.m. |
| NEDg | Description generation | batch_69c604d3839081909f98c37f0fe8f0af |
completed | March 27, 2026, 4:17 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6054d2e388190a4bafffce879b039 |
completed | March 27, 2026, 4:19 a.m. |
Created at: March 22, 2026, 4:30 p.m.