Triple
T22901841
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oskarshamn Municipality |
E568338
|
entity |
| Predicate | hasSeat |
P3522
|
FINISHED |
| Object | Oskarshamn |
—
|
NE NERFINISHED |
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: Oskarshamn | Statement: [Oskarshamn Municipality, hasSeat, Oskarshamn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oskarshamn Context triple: [Oskarshamn Municipality, hasSeat, Oskarshamn]
-
A.
Oskarshamn
chosen
Oskarshamn is a coastal town in southeastern Sweden known for its Baltic Sea harbor and proximity to the island of Gotland.
-
B.
Karlshamn
Karlshamn is a coastal town in southern Sweden known for its harbor, archipelago, and role as a regional industrial and transport hub.
-
C.
Eskilstuna
Eskilstuna is an industrial city in central Sweden known historically for its metalworking and engineering industries.
-
D.
Söderhamn
Söderhamn is a coastal town in east-central Sweden known for its historical wooden architecture and role as the administrative and commercial center of the surrounding region.
-
E.
Fagersta
Fagersta is an industrial town in central Sweden known for its steel production and manufacturing heritage.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e2458cd9e48190943ad2e34485d939 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18016d8e481908fc47d003156b800 |
completed | April 29, 2026, 3:50 a.m. |
Created at: April 17, 2026, 3:41 p.m.