Triple
T12585742
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wigger |
E300454
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Zofingen |
E951316
|
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: Zofingen | Statement: [Wigger, flowsThrough, Zofingen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zofingen Context triple: [Wigger, flowsThrough, Zofingen]
-
A.
Zofingen
chosen
Zofingen is a historic Swiss town in the canton of Aargau known for its well-preserved old town and annual open-air festival.
-
B.
Waldegg
Waldegg is a locality in Switzerland situated along the route of the A3 motorway.
-
C.
Attiswil
Attiswil is a municipality in the canton of Bern in Switzerland, located in the Oberaargau region.
-
D.
Bremgarten
Bremgarten is a historic Swiss town in the canton of Aargau, known for its well-preserved medieval old town and scenic riverside setting.
-
E.
Zollikon
Zollikon is an affluent suburban municipality on the shores of Lake Zurich, known for its residential character and proximity to the city of Zurich in Switzerland.
- 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_69d7bde87b648190bcd0266e9efde098 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d954bbe72c8190aa11090bb6b480c9 |
completed | April 10, 2026, 7:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6c0d89b988190bde04701c4d1f904 |
completed | May 3, 2026, 3:28 a.m. |
Created at: April 9, 2026, 5:04 p.m.