Triple
T17505087
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dyrøya |
E426292
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Troms |
—
|
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: Troms | Statement: [Dyrøya, region, Troms]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Troms Context triple: [Dyrøya, region, Troms]
-
A.
Troms
chosen
Troms was a former county in northern Norway known for its Arctic landscapes, coastal fjords, and the city of Tromsø.
-
B.
Trondenes
Trondenes is a historic former municipality and parish in northern Norway, known for its medieval stone church and role as an administrative center in the Harstad region.
-
C.
Tjørhom
Tjørhom is a small village in southwestern Norway known for its mountainous landscape and proximity to popular skiing and outdoor recreation areas.
-
D.
Sør-Troms
Sør-Troms is a district in northern Norway encompassing several coastal and inland municipalities in the southern part of Troms county.
-
E.
Tjuneroy
Tjuneroy was an ancient Egyptian official, likely a high-ranking scribe or priest under Ramesses II, associated with the creation of the Saqqara King List.
- 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_69d889dd9164819087b1dc3c9240c870 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e45214d44c8190b1bf04bf24ab8e81 |
completed | April 19, 2026, 3:55 a.m. |
Created at: April 10, 2026, 5:48 a.m.