Triple
T2206989
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sarpsborg |
E50822
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Østfold region |
E96296
|
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: Østfold region | Statement: [Sarpsborg, locatedIn, Østfold region]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Østfold region Context triple: [Sarpsborg, locatedIn, Østfold region]
-
A.
Østfold
chosen
Østfold is a former county in southeastern Norway known for its coastal landscape along the Oslofjord, historic fortresses, and proximity to the Swedish border.
-
B.
Østlandet
Østlandet is the most populous region of southeastern Norway, encompassing the capital city Oslo and surrounding inland and coastal areas.
-
C.
Agder
Agder is a county in southern Norway known for its long coastline, maritime heritage, and popular coastal towns and islands.
-
D.
Vestfold og Telemark
Vestfold og Telemark is a former county in southeastern Norway known for its coastal towns, industrial heritage, and varied landscapes from fjords to inland forests and mountains.
-
E.
Aust-Agder
Aust-Agder was a former county in southern Norway known for its coastal towns, forests, and role in the country’s maritime and timber industries.
- 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_69a88b06709c8190978fb2418470d1b6 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abbfcbb83081908d5b2f1603c7b4d2 |
completed | March 7, 2026, 6:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aeb3b515c081909d6ad7f0506ea5a8 |
completed | March 9, 2026, 11:49 a.m. |
Created at: March 4, 2026, 7:46 p.m.