Triple
T1138626
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oslo Metro |
E23196
|
entity |
| Predicate | terminus |
P388
|
FINISHED |
| Object |
Ringen via Tøyen
Ringen via Tøyen is a circular service pattern on the Oslo Metro that routes trains through Tøyen station before completing a loop.
|
E129527
|
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: Ringen via Tøyen | Statement: [Oslo Metro, terminus, Ringen via Tøyen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ringen via Tøyen Context triple: [Oslo Metro, terminus, Ringen via Tøyen]
-
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.
Kongsseteren
Kongsseteren is a historic winter residence and retreat used by the Norwegian royal family near Oslo.
-
C.
Oksskolten
Oksskolten is the highest mountain in Northern Norway, known for its prominent peak in the Okstindan range.
-
D.
Berg en Dal
Berg en Dal is a Dutch municipality in the province of Gelderland, known for its hilly landscape, forests, and proximity to the city of Nijmegen.
-
E.
Trou-ringh
Trou-ringh is a didactic emblem book by Dutch poet and moralist Jacob Cats that offers moral lessons through allegorical illustrations and verse.
- 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: Ringen via Tøyen Triple: [Oslo Metro, terminus, Ringen via Tøyen]
Generated description
Ringen via Tøyen is a circular service pattern on the Oslo Metro that routes trains through Tøyen station before completing a loop.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ringen via Tøyen Target entity description: Ringen via Tøyen is a circular service pattern on the Oslo Metro that routes trains through Tøyen station before completing a loop.
-
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.
Kongsseteren
Kongsseteren is a historic winter residence and retreat used by the Norwegian royal family near Oslo.
-
C.
Oksskolten
Oksskolten is the highest mountain in Northern Norway, known for its prominent peak in the Okstindan range.
-
D.
Berg en Dal
Berg en Dal is a Dutch municipality in the province of Gelderland, known for its hilly landscape, forests, and proximity to the city of Nijmegen.
-
E.
Trou-ringh
Trou-ringh is a didactic emblem book by Dutch poet and moralist Jacob Cats that offers moral lessons through allegorical illustrations and verse.
- 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_69a493ec75988190b63a11bafaec29b4 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4bc25dda481909a26d726fdbdbb50 |
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.