Triple
T4478470
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Örebro County |
E100067
|
entity |
| Predicate | historicalProvince |
P915
|
FINISHED |
| Object | Närke |
E340035
|
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: Närke | Statement: [Örebro County, historicalProvince, Närke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Närke Context triple: [Örebro County, historicalProvince, Närke]
-
A.
Närke
chosen
Närke is a historical province in central Sweden known for its Central Swedish dialects and its location around the city of Örebro.
-
B.
Blekinge
Blekinge is a historical province in southern Sweden on the Baltic Sea coast, known for its archipelago, maritime heritage, and strategic location.
-
C.
Bohuslän
Bohuslän is a coastal province in western Sweden known for its rugged granite shoreline, fishing villages, and archipelago along the Skagerrak.
-
D.
Uppland
Uppland is a historical province in east-central Sweden that includes parts of the greater Stockholm area and key infrastructure such as Stockholm Arlanda Airport.
-
E.
Dalsland
Dalsland is a historical province in western Sweden known for its forests, lakes, and rural landscapes.
- 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_69b34553cbe48190afa8ac1cac285b86 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b356db0a008190ad39b68efc095b8d |
completed | March 13, 2026, 12:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b63776ae008190a7668cddf1a42df3 |
completed | March 15, 2026, 4:37 a.m. |
Created at: March 12, 2026, 11:35 p.m.