Triple
T21117015
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Into the White |
E520325
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object | Petter Næss |
—
|
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: Petter Næss | Statement: [Into the White, screenwriter, Petter Næss]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Petter Næss Context triple: [Into the White, screenwriter, Petter Næss]
-
A.
Petter Næss
chosen
Petter Næss is a Norwegian film director and actor best known internationally for his work on character-driven dramas and comedies.
-
B.
Atle Næss
Atle Næss is a Norwegian author and historian known for his novels and biographical works, particularly on scientific and cultural figures.
-
C.
Erling Bjørnson
Erling Bjørnson was a Norwegian farmer and politician, best known as the son of Nobel Prize–winning writer Bjørnstjerne Bjørnson.
-
D.
Einar Lie
Einar Lie is a Norwegian economic historian and professor known for his research on Norway’s economic policy, financial history, and the development of the welfare state.
-
E.
Einar Bjørnson
Einar Bjørnson was a Norwegian figure known primarily as the son of Nobel Prize–winning writer and national icon Bjørnstjerne Bjørnson.
- 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_69e0b50a623881909c0bbaf4f2c055e7 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e72106a3b48190a0efa51a74ae21f0 |
completed | April 21, 2026, 7:02 a.m. |
Created at: April 16, 2026, 2:55 p.m.