Triple
T1139715
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oslo Tramway |
E23420
|
entity |
| Predicate | officialName |
P66
|
FINISHED |
| Object |
Trikken i Oslo
Trikken i Oslo is the electric tram system that forms a key part of the public transport network in Norway’s capital, Oslo.
|
E129584
|
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: Trikken i Oslo | Statement: [Oslo Tramway, officialName, Trikken i Oslo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Trikken i Oslo Context triple: [Oslo Tramway, officialName, Trikken i Oslo]
-
A.
Mo i Rana
Mo i Rana is an industrial town in Nordland county, Norway, known for its steel industry, proximity to the Arctic Circle, and role as a regional hub in Northern Norway.
-
B.
Drammen
Drammen is a city and municipality in southeastern Norway known for its riverside setting along the Drammenselva and its role as a regional commercial and transport hub.
-
C.
Oksskolten
Oksskolten is the highest mountain in Northern Norway, known for its prominent peak in the Okstindan range.
-
D.
Tøyen
Tøyen is a neighborhood in Oslo, Norway, known for its cultural institutions, parks, and educational facilities.
-
E.
Øyer
Øyer is a small municipality in Innlandet county, Norway, known for its rural valley landscape and proximity to the Hafjell ski resort.
- 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: Trikken i Oslo Triple: [Oslo Tramway, officialName, Trikken i Oslo]
Generated description
Trikken i Oslo is the electric tram system that forms a key part of the public transport network in Norway’s capital, Oslo.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Trikken i Oslo Target entity description: Trikken i Oslo is the electric tram system that forms a key part of the public transport network in Norway’s capital, Oslo.
-
A.
Mo i Rana
Mo i Rana is an industrial town in Nordland county, Norway, known for its steel industry, proximity to the Arctic Circle, and role as a regional hub in Northern Norway.
-
B.
Drammen
Drammen is a city and municipality in southeastern Norway known for its riverside setting along the Drammenselva and its role as a regional commercial and transport hub.
-
C.
Oksskolten
Oksskolten is the highest mountain in Northern Norway, known for its prominent peak in the Okstindan range.
-
D.
Tøyen
Tøyen is a neighborhood in Oslo, Norway, known for its cultural institutions, parks, and educational facilities.
-
E.
Øyer
Øyer is a small municipality in Innlandet county, Norway, known for its rural valley landscape and proximity to the Hafjell ski resort.
- 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_69a493ef399c8190b04b9146d2314f59 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4bc27c88881909c64ec30b7f66575 |
completed | March 1, 2026, 10:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac59b020d48190bc6ecbdb720c6779 |
completed | March 7, 2026, 5 p.m. |
| NEDg | Description generation | batch_69ac5a7599048190a46b0d560270ffa4 |
completed | March 7, 2026, 5:03 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac5af24a948190a37c832508149a48 |
completed | March 7, 2026, 5:05 p.m. |
Created at: March 1, 2026, 7:44 p.m.