Triple
T4365607
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oppland |
E98763
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | Lillehammer |
E17762
|
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: Lillehammer | Statement: [Oppland, hasMajorCity, Lillehammer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lillehammer Context triple: [Oppland, hasMajorCity, Lillehammer]
-
A.
Lillehammer
chosen
Lillehammer is a Norwegian town in the Gudbrandsdalen valley, best known internationally for staging the 1994 Winter Olympics.
-
B.
Trondheim
Trondheim is a historic Norwegian city in Trøndelag county, known for its medieval Nidaros Cathedral and role as a former capital of Norway.
-
C.
Tromsø
Tromsø is a city in northern Norway known for its Arctic location, vibrant cultural scene, and prominence as a viewing spot for the Northern Lights.
-
D.
Lørenskog
Lørenskog is a suburban municipality in Viken county, Norway, located just east of Oslo and known for its residential areas and commercial centers.
-
E.
Bodø
Bodø is a coastal city in northern Norway known as a regional hub for culture, transport, and access to Arctic nature.
- 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_69b3454c772081908e20173e379e8ebe |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35200263081909bb326a4d7a8db99 |
completed | March 12, 2026, 11:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be777b514081909832a8a520a7f7d1 |
completed | March 21, 2026, 10:48 a.m. |
Created at: March 12, 2026, 11:17 p.m.