Triple
T5705872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Asker |
E125781
|
entity |
| Predicate | hasSportsClub |
P346
|
FINISHED |
| Object |
Frisk Asker
Frisk Asker is a Norwegian sports club best known for its ice hockey team, which competes at the top level of Norwegian hockey.
|
E544365
|
NE FINISHED |
How this triple was built (4 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: Frisk Asker | Statement: [Asker, hasSportsClub, Frisk Asker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Frisk Asker Context triple: [Asker, hasSportsClub, Frisk Asker]
-
A.
Asker Fotball
Asker Fotball is a Norwegian football club based in Asker, known for competing in the national league system and developing local talent.
-
B.
Vålerenga
Vålerenga is a neighborhood in Oslo, Norway, known for its working-class roots and strong association with the local football club Vålerenga Fotball.
-
C.
Mjøndalen
Mjøndalen is a town in Viken county, Norway, known historically for its industry and for its football club Mjøndalen IF.
-
D.
Fredrikstad FK
Fredrikstad FK is a Norwegian professional football club based in the city of Fredrikstad, known for its historic success in the national league and cup competitions.
-
E.
Bryne FK
Bryne FK is a Norwegian football club known for developing striker Erling Haaland in its youth system.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Frisk Asker Triple: [Asker, hasSportsClub, Frisk Asker]
Generated description
Frisk Asker is a Norwegian sports club best known for its ice hockey team, which competes at the top level of Norwegian hockey.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Frisk Asker Target entity description: Frisk Asker is a Norwegian sports club best known for its ice hockey team, which competes at the top level of Norwegian hockey.
-
A.
Asker Fotball
Asker Fotball is a Norwegian football club based in Asker, known for competing in the national league system and developing local talent.
-
B.
Vålerenga
Vålerenga is a neighborhood in Oslo, Norway, known for its working-class roots and strong association with the local football club Vålerenga Fotball.
-
C.
Mjøndalen
Mjøndalen is a town in Viken county, Norway, known historically for its industry and for its football club Mjøndalen IF.
-
D.
Fredrikstad FK
Fredrikstad FK is a Norwegian professional football club based in the city of Fredrikstad, known for its historic success in the national league and cup competitions.
-
E.
Bryne FK
Bryne FK is a Norwegian football club known for developing striker Erling Haaland in its youth system.
- F. None of above. chosen
Provenance (5 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_69c0082d6fe48190b777fb383769e5c8 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c02459cd18819080fda0b481d11f08 |
completed | March 22, 2026, 5:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c07de7df8c8190824d24f729eaa04d |
completed | March 22, 2026, 11:40 p.m. |
| NEDg | Description generation | batch_69c08b820a048190b3874522d568d485 |
completed | March 23, 2026, 12:38 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c08be237a88190ace6e3d4ab97bf17 |
completed | March 23, 2026, 12:40 a.m. |
Created at: March 22, 2026, 3:45 p.m.