Triple
T5291068
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eliteserien |
E119741
|
entity |
| Predicate | formerName |
P65
|
FINISHED |
| Object |
Tippeligaen
Tippeligaen was the former sponsored name of Norway’s top-tier professional football league, now known as Eliteserien.
|
E508962
|
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: Tippeligaen | Statement: [Eliteserien, formerName, Tippeligaen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tippeligaen Context triple: [Eliteserien, formerName, Tippeligaen]
-
A.
Vålerenga
Vålerenga is a neighborhood in Oslo, Norway, known for its working-class roots and strong association with the local football club Vålerenga Fotball.
-
B.
Bryne FK
Bryne FK is a Norwegian football club known for developing striker Erling Haaland in its youth system.
-
C.
Mjøndalen
Mjøndalen is a town in Viken county, Norway, known historically for its industry and for its football club Mjøndalen IF.
-
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.
Tromsø IL
Tromsø IL is a Norwegian professional football club based in the city of Tromsø, known for competing in the country’s top divisions and for being one of the world’s northernmost elite clubs.
- 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: Tippeligaen Triple: [Eliteserien, formerName, Tippeligaen]
Generated description
Tippeligaen was the former sponsored name of Norway’s top-tier professional football league, now known as Eliteserien.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tippeligaen Target entity description: Tippeligaen was the former sponsored name of Norway’s top-tier professional football league, now known as Eliteserien.
-
A.
Vålerenga
Vålerenga is a neighborhood in Oslo, Norway, known for its working-class roots and strong association with the local football club Vålerenga Fotball.
-
B.
Bryne FK
Bryne FK is a Norwegian football club known for developing striker Erling Haaland in its youth system.
-
C.
Mjøndalen
Mjøndalen is a town in Viken county, Norway, known historically for its industry and for its football club Mjøndalen IF.
-
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.
Tromsø IL
Tromsø IL is a Norwegian professional football club based in the city of Tromsø, known for competing in the country’s top divisions and for being one of the world’s northernmost elite clubs.
- 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_69bd446de5648190b313a90bd96730d2 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd84eac7b88190900142bd1310c0fd |
completed | March 20, 2026, 5:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf06f066988190a3df7e270df84fdd |
completed | March 21, 2026, 9 p.m. |
| NEDg | Description generation | batch_69bf09fd3dc48190be94a4b5b1f32dc6 |
completed | March 21, 2026, 9:13 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bf0a8c90888190bd7cc6d940b62cd1 |
completed | March 21, 2026, 9:15 p.m. |
Created at: March 20, 2026, 1:52 p.m.