Triple
T153468
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fritt Ord Award |
E3480
|
entity |
| Predicate | location |
P40
|
FINISHED |
| Object | Oslo |
E3654
|
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: Oslo | Statement: [Fritt Ord Award, location, Oslo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oslo Context triple: [Fritt Ord Award, location, Oslo]
-
A.
Oslo
chosen
Oslo is the capital and largest city of Norway, known as a major cultural, economic, and governmental center.
-
B.
Lillehammer
Lillehammer is a Norwegian town in the Gudbrandsdalen valley, best known internationally for staging the 1994 Winter Olympics.
-
C.
Copenhagen
Copenhagen is the capital and largest city of Denmark, known for its historic architecture, vibrant cultural scene, and high quality of life.
-
D.
Stockholm
Stockholm is the capital city of Sweden, renowned for its historic architecture, cultural institutions, and role as a major political, economic, and scientific center in Scandinavia.
-
E.
Gothenburg
Gothenburg is Sweden’s second-largest city, a major port on the country’s west coast known for its maritime heritage, universities, and vibrant cultural scene.
- 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_69a252868de4819080e21c9938bfe8b6 |
completed | Feb. 28, 2026, 2:27 a.m. |
| NER | Named-entity recognition | batch_69a25810799c8190a515a39169126e46 |
completed | Feb. 28, 2026, 2:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a31619063c8190879a726867d4b46d |
completed | Feb. 28, 2026, 4:21 p.m. |
Created at: Feb. 28, 2026, 2:31 a.m.