Triple
T1354106
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Norwegian Air Shuttle |
E28947
|
entity |
| Predicate | frequentFlyerProgram |
P178
|
FINISHED |
| Object | Norwegian Reward |
E156092
|
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: Norwegian Reward | Statement: [Norwegian Air Shuttle, frequentFlyerProgram, Norwegian Reward]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Norwegian Reward Context triple: [Norwegian Air Shuttle, frequentFlyerProgram, Norwegian Reward]
-
A.
Norwegian Reward
chosen
Norwegian Reward is the frequent flyer loyalty program of low-cost airline Norwegian Air Shuttle, offering members points and travel-related benefits.
-
B.
Solbo
Solbo is a locality within Botkyrka Municipality in Stockholm County, Sweden.
-
C.
Troms
Troms was a former county in northern Norway known for its Arctic landscapes, coastal fjords, and the city of Tromsø.
-
D.
Øyer
Øyer is a small municipality in Innlandet county, Norway, known for its rural valley landscape and proximity to the Hafjell ski resort.
-
E.
Kongsseteren
Kongsseteren is a historic winter residence and retreat used by the Norwegian royal family near Oslo.
- 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_69a498571d248190a0ac9eb02d97097f |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c26e916c8190b4b324df87f4c121 |
completed | March 1, 2026, 10:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acd47b63c081908a859a88ad5564b8 |
completed | March 8, 2026, 1:44 a.m. |
Created at: March 1, 2026, 7:56 p.m.