Triple
T5291130
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | VIF |
E119742
|
entity |
| Predicate | hasRival |
P1375
|
FINISHED |
| Object | Lyn Fotball |
E22611
|
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: Lyn Fotball | Statement: [VIF, hasRival, Lyn Fotball]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lyn Fotball Context triple: [VIF, hasRival, Lyn Fotball]
-
A.
Lyn Fotball
chosen
Lyn Fotball is a Norwegian football club based in Oslo, historically known as one of the country’s oldest and most traditional teams.
-
B.
FK Gjøvik-Lyn
FK Gjøvik-Lyn is a Norwegian football club based in the town of Gjøvik, competing in the national league system.
-
C.
Skeid Fotball
Skeid Fotball is a Norwegian football club from Oslo known for its historic presence in the national leagues and local rivalries with other Oslo teams.
-
D.
Lillestrøm
Lillestrøm is a Norwegian town and former municipality in the Greater Oslo Region, known as a regional commercial center and transport hub.
-
E.
Vålerenga
Vålerenga is a neighborhood in Oslo, Norway, known for its working-class roots and strong association with the local football club Vålerenga Fotball.
- 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_69bd446de5648190b313a90bd96730d2 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd84eccac481908ba3fe28c3908d1d |
completed | March 20, 2026, 5:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf06f066988190a3df7e270df84fdd |
completed | March 21, 2026, 9 p.m. |
Created at: March 20, 2026, 1:52 p.m.