Triple
T4365707
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Drammenselva |
E98765
|
entity |
| Predicate | passesNear |
P416
|
FINISHED |
| Object |
Mjøndalen
Mjøndalen is a town in Viken county, Norway, known historically for its industry and for its football club Mjøndalen IF.
|
E433548
|
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: Mjøndalen | Statement: [Drammenselva, passesNear, Mjøndalen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mjøndalen Context triple: [Drammenselva, passesNear, Mjøndalen]
-
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.
Lillestrøm
Lillestrøm is a Norwegian town and former municipality in the Greater Oslo Region, known as a regional commercial center and transport hub.
-
C.
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.
-
D.
Bryne FK
Bryne FK is a Norwegian football club known for developing striker Erling Haaland in its youth system.
-
E.
Lillestrøm SK
Lillestrøm SK is a Norwegian professional football club known for its passionate fan base, historic success in domestic competitions, and intense rivalry with other Oslo-area teams.
- 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: Mjøndalen Triple: [Drammenselva, passesNear, Mjøndalen]
Generated description
Mjøndalen is a town in Viken county, Norway, known historically for its industry and for its football club Mjøndalen IF.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mjøndalen Target entity description: Mjøndalen is a town in Viken county, Norway, known historically for its industry and for its football club Mjøndalen IF.
-
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.
Lillestrøm
Lillestrøm is a Norwegian town and former municipality in the Greater Oslo Region, known as a regional commercial center and transport hub.
-
C.
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.
-
D.
Bryne FK
Bryne FK is a Norwegian football club known for developing striker Erling Haaland in its youth system.
-
E.
Lillestrøm SK
Lillestrøm SK is a Norwegian professional football club known for its passionate fan base, historic success in domestic competitions, and intense rivalry with other Oslo-area teams.
- 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_69b3454c772081908e20173e379e8ebe |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35200263081909bb326a4d7a8db99 |
completed | March 12, 2026, 11:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5dbcbbd1881908eb9f0ea6b2fe16b |
completed | March 14, 2026, 10:06 p.m. |
| NEDg | Description generation | batch_69b5dcf36dfc8190847925dbed92c059 |
completed | March 14, 2026, 10:10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5ddad45b8819082ac7a3a9c5f2f07 |
completed | March 14, 2026, 10:14 p.m. |
Created at: March 12, 2026, 11:17 p.m.