Triple
T3701704
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nordland |
E80792
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Mo i Rana |
E74442
|
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: Mo i Rana | Statement: [Nordland, hasCity, Mo i Rana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mo i Rana Context triple: [Nordland, hasCity, Mo i Rana]
-
A.
Mo i Rana
chosen
Mo i Rana is an industrial town in Nordland county, Norway, known for its steel industry, proximity to the Arctic Circle, and role as a regional hub in Northern Norway.
-
B.
Lågen
Lågen is a major river in southeastern Norway that flows through the Gudbrandsdalen valley before joining the Mjøsa lake.
-
C.
Løten
Løten is a rural municipality in Innlandet county, Norway, known for its agricultural landscape and historic connections to painter Edvard Munch.
-
D.
Märsta
Märsta is a town in Stockholm County, Sweden, known as a residential and transport hub near Stockholm Arlanda Airport.
-
E.
Berg en Dal
Berg en Dal is a Dutch municipality in the province of Gelderland, known for its hilly landscape, forests, and proximity to the city of Nijmegen.
- 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_69ad8b1793888190a5f70e4b21dc05a1 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adc547c1848190a1ece46c59b7c43d |
completed | March 8, 2026, 6:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4cdf53190819098529d11a5a3c7a8 |
completed | March 14, 2026, 2:54 a.m. |
Created at: March 8, 2026, 3:33 p.m.