Triple
T15618223
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haugesund municipality |
E375473
|
entity |
| Predicate | hasCinema |
P1060
|
FINISHED |
| Object | Edda Kino Haugesund |
E1149858
|
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: Edda Kino Haugesund | Statement: [Haugesund municipality, hasCinema, Edda Kino Haugesund]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Edda Kino Haugesund Context triple: [Haugesund municipality, hasCinema, Edda Kino Haugesund]
-
A.
Edda Kino, Haugesund
chosen
Edda Kino in Haugesund is a prominent cinema complex in Norway best known for hosting the main screenings of the Norwegian International Film Festival.
-
B.
Sogndal
Sogndal is a village and municipality in Vestland county, Norway, known for its scenic fjord landscape, agriculture, and as a regional education and service center.
-
C.
Bjug Harstad
Bjug Harstad was a Norwegian-American Lutheran minister and educator best known for establishing Pacific Lutheran University in Washington State.
-
D.
Raufoss
Raufoss is an industrial town in Norway known for its manufacturing sector, particularly in defense and automotive components.
-
E.
Egersund
Egersund is a coastal town in southwestern Norway known for its fishing industry, historic wooden architecture, and scenic harbor.
- 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_69d85ccf2794819096cda4cbcb02d478 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e997ce481909b2f10d25705fbc6 |
completed | April 16, 2026, 2:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff56def20881909f835dd44ab9ac2b |
completed | May 9, 2026, 3:46 p.m. |
Created at: April 10, 2026, 4:13 a.m.