Triple
T15836936
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rygge Air Station |
E384008
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Rygge |
E244459
|
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: Rygge | Statement: [Rygge Air Station, locatedIn, Rygge]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rygge Context triple: [Rygge Air Station, locatedIn, Rygge]
-
A.
Rygge
chosen
Rygge is a municipality in southeastern Norway, historically known for its military air station and proximity to the town of Moss.
-
B.
Sakshaug
Sakshaug is a village in the municipality of Inderøy in Trøndelag county, Norway, known for its historic church and rural setting.
-
C.
Ekornes
Ekornes is a Norwegian furniture manufacturer best known for its Stressless line of reclining chairs and sofas.
-
D.
Torbjørn
Torbjørn is a Scandinavian masculine given name, particularly common in Norway, derived from Old Norse elements meaning "Thor" and "bear."
-
E.
Vilailuck Teigen
Vilailuck Teigen is a Thai-American television personality and social media figure best known as the mother of model and author Chrissy Teigen.
- 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_69d86da34c888190976e06c4019d415a |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e142e1fcd48190bcb884f6c65db847 |
completed | April 16, 2026, 8:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffa13a83448190adcad8bb84622e55 |
completed | May 9, 2026, 9:03 p.m. |
Created at: April 10, 2026, 4:49 a.m.