Triple
T10297410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Västmanland County |
E241527
|
entity |
| Predicate | hasUrbanArea |
P316
|
FINISHED |
| Object | Köping |
E527084
|
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: Köping | Statement: [Västmanland County, hasUrbanArea, Köping]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Köping Context triple: [Västmanland County, hasUrbanArea, Köping]
-
A.
Köping
chosen
Köping is a small industrial town in central Sweden known for its manufacturing heritage and location along the Köping River in Västmanland County.
-
B.
Nyköping
Nyköping is a historic coastal town in southeastern Sweden known for its medieval castle, harbor, and role as a regional administrative and cultural center.
-
C.
Norrköping
Norrköping is a historic industrial city in eastern Sweden known for its preserved textile mills, waterways, and cultural institutions.
-
D.
Jönköping
Jönköping is a city in southern Sweden, located at the southern end of Lake Vättern and known as a regional commercial and logistical hub.
-
E.
Enköping
Enköping is a small Swedish town known for its numerous themed parks and gardens, often called “Sweden’s nearest town” due to its central location relative to several major cities.
- 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_69d381aaafc08190af475ef58dc16aba |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d2ebd258819099fadddcd13099fc |
completed | April 7, 2026, 9:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69deb028c0788190ae8d6750f2f9634e |
completed | April 14, 2026, 9:22 p.m. |
Created at: April 6, 2026, 11:43 a.m.