Triple
T4476948
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Namsos |
E100031
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Trøndelag county |
E17971
|
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: Trøndelag county | Statement: [Namsos, partOf, Trøndelag county]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Trøndelag county Context triple: [Namsos, partOf, Trøndelag county]
-
A.
Hedmark
Hedmark is a former county in eastern Norway known for its vast forests, agriculture, and inland landscapes along the Swedish border.
-
B.
Sogn og Fjordane
Sogn og Fjordane was a former county in western Norway known for its dramatic fjords, mountains, and coastal landscapes.
-
C.
Trøndelag
chosen
Trøndelag is a central region of Norway known for its historic city of Trondheim, coastal landscapes, and strong cultural traditions.
-
D.
Hedmarken
Hedmarken is a traditional district in Innlandet county in eastern Norway, known for its agricultural landscapes and its central town, Hamar.
-
E.
Nordland county
Nordland county is a long, coastal region in northern Norway known for its dramatic fjords, islands, and Arctic 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_69b356d8600c8190a8b812889c50f144 |
completed | March 13, 2026, 12:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf7fc5ae2c819085a18354feaa6150 |
completed | March 22, 2026, 5:36 a.m. |
Created at: March 12, 2026, 11:35 p.m.