Triple
T5392634
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Biberist |
E120368
|
entity |
| Predicate | neighboringMunicipality |
P17964
|
FINISHED |
| Object |
Gerlafingen
Gerlafingen is a municipality in the canton of Solothurn in Switzerland, known historically for its steel industry and residential character.
|
E520970
|
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: Gerlafingen | Statement: [Biberist, neighboringMunicipality, Gerlafingen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gerlafingen Context triple: [Biberist, neighboringMunicipality, Gerlafingen]
-
A.
Andelfingen
Andelfingen is a municipality and regional center in the canton of Zürich in northern Switzerland, known for its rural character and vineyards along the Thur River.
-
B.
Bettlach
Bettlach is a Swiss municipality located in the canton of Solothurn.
-
C.
Besseggen
Besseggen is a famous mountain ridge and hiking route in Norway known for its dramatic views between the lakes Gjende and Bessvatnet.
-
D.
Gimmelwald
Gimmelwald is a small, traditional Swiss alpine village known for its dramatic mountain scenery and tranquil, car-free atmosphere in the Bernese Oberland.
-
E.
Faulensee
Faulensee is a lakeside village in the Swiss canton of Bern, situated on the southern shore of Lake Thun and known for its scenic Alpine setting.
- 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: Gerlafingen Triple: [Biberist, neighboringMunicipality, Gerlafingen]
Generated description
Gerlafingen is a municipality in the canton of Solothurn in Switzerland, known historically for its steel industry and residential character.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gerlafingen Target entity description: Gerlafingen is a municipality in the canton of Solothurn in Switzerland, known historically for its steel industry and residential character.
-
A.
Andelfingen
Andelfingen is a municipality and regional center in the canton of Zürich in northern Switzerland, known for its rural character and vineyards along the Thur River.
-
B.
Bettlach
Bettlach is a Swiss municipality located in the canton of Solothurn.
-
C.
Besseggen
Besseggen is a famous mountain ridge and hiking route in Norway known for its dramatic views between the lakes Gjende and Bessvatnet.
-
D.
Gimmelwald
Gimmelwald is a small, traditional Swiss alpine village known for its dramatic mountain scenery and tranquil, car-free atmosphere in the Bernese Oberland.
-
E.
Faulensee
Faulensee is a lakeside village in the Swiss canton of Bern, situated on the southern shore of Lake Thun and known for its scenic Alpine setting.
- 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_69bd46354c648190a38b26f107010a96 |
completed | March 20, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69bd8719ff04819089e3a90f90b5e3fc |
completed | March 20, 2026, 5:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf411d608c8190a672e336e6f3954d |
completed | March 22, 2026, 1:08 a.m. |
| NEDg | Description generation | batch_69bf4214143c8190a104c16ecd664a7d |
completed | March 22, 2026, 1:12 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69bf426f81f88190acfd2255599b3aeb |
completed | March 22, 2026, 1:14 a.m. |
Created at: March 20, 2026, 2:04 p.m.