Triple
T20018018
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Langevåg |
E494772
|
entity |
| Predicate | hasSportsClub |
P346
|
FINISHED |
| Object |
Langevåg IL
Langevåg IL is a Norwegian multi-sport club based in Langevåg, known primarily for its local football activities and community sports programs.
|
E1407166
|
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: Langevåg IL | Statement: [Langevåg, hasSportsClub, Langevåg IL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Langevåg IL Context triple: [Langevåg, hasSportsClub, Langevåg IL]
-
A.
Kjelsås IL
Kjelsås IL is a Norwegian multi-sport club based in the Kjelsås neighborhood of Oslo, known for activities such as football, handball, and skiing.
-
B.
Hokksund IL
Hokksund IL is a Norwegian multi-sport club based in Hokksund, known for organizing local football and other athletic activities.
-
C.
Lillehammer IK
Lillehammer IK is a Norwegian ice hockey club based in Lillehammer that competes in the country’s top leagues.
-
D.
Drøbak-Frogn IL
Drøbak-Frogn IL is a Norwegian sports club best known for its football program, which helped develop future international star Martin Ødegaard in his youth.
-
E.
Hasle-Løren IL
Hasle-Løren IL is a Norwegian sports club from Oslo known for its ice hockey program that helped develop NHL player Mats Zuccarello.
- 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: Langevåg IL Triple: [Langevåg, hasSportsClub, Langevåg IL]
Generated description
Langevåg IL is a Norwegian multi-sport club based in Langevåg, known primarily for its local football activities and community sports programs.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Langevåg IL Target entity description: Langevåg IL is a Norwegian multi-sport club based in Langevåg, known primarily for its local football activities and community sports programs.
-
A.
Kjelsås IL
Kjelsås IL is a Norwegian multi-sport club based in the Kjelsås neighborhood of Oslo, known for activities such as football, handball, and skiing.
-
B.
Hokksund IL
Hokksund IL is a Norwegian multi-sport club based in Hokksund, known for organizing local football and other athletic activities.
-
C.
Lillehammer IK
Lillehammer IK is a Norwegian ice hockey club based in Lillehammer that competes in the country’s top leagues.
-
D.
Drøbak-Frogn IL
Drøbak-Frogn IL is a Norwegian sports club best known for its football program, which helped develop future international star Martin Ødegaard in his youth.
-
E.
Hasle-Løren IL
Hasle-Løren IL is a Norwegian sports club from Oslo known for its ice hockey program that helped develop NHL player Mats Zuccarello.
- 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_69da626bfd288190aa5d65098b6433ae |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6623d76808190988990a8dc263ef7 |
completed | April 20, 2026, 5:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a080e286230819097e564d028e73ab5 |
completed | May 16, 2026, 6:26 a.m. |
| NEDg | Description generation | batch_6a080ef87f08819091b4a76009b1c4ba |
completed | May 16, 2026, 6:30 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a080f8bb81c819098d39ad6e1642aa8 |
completed | May 16, 2026, 6:32 a.m. |
Created at: April 11, 2026, 3:34 p.m.