Triple
T1573778
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ofotfjord |
E33601
|
entity |
| Predicate | nearestCity |
P350
|
FINISHED |
| Object | Narvik |
E33599
|
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: Narvik | Statement: [Ofotfjord, nearestCity, Narvik]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Narvik Context triple: [Ofotfjord, nearestCity, Narvik]
-
A.
Narvik
chosen
Narvik is a port town in northern Norway known for its strategic importance during World War II and as the site of major naval and land battles.
-
B.
Kiruna
Kiruna is a mining town in northern Sweden known for its large iron ore mine and its location above the Arctic Circle.
-
C.
Notodden
Notodden is a town and municipality in Vestfold og Telemark county, Norway, known for its industrial heritage and annual blues festival.
-
D.
Røros
Røros is a historic Norwegian mining town and UNESCO World Heritage Site known for its well-preserved wooden buildings and copper mining heritage.
-
E.
Kirkenes
Kirkenes is a remote Arctic town in northeastern Norway, near the Russian border, known for its Barents Sea port, winter tourism, and role as a gateway to the far north.
- 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_69a885f27a4c8190a4622252cdf54c00 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a908bcd87881908b911314a30dd327 |
completed | March 5, 2026, 4:38 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69add1a893fc8190832442b1b0937d69 |
completed | March 8, 2026, 7:44 p.m. |
Created at: March 4, 2026, 7:27 p.m.