Triple
T7394574
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oslo Metro Line 1 |
E170589
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object |
Brynseng
Brynseng is a neighborhood and transport hub in Oslo, Norway, served by the Oslo Metro and other public transit connections.
|
E661128
|
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: Brynseng | Statement: [Oslo Metro Line 1, passesThrough, Brynseng]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brynseng Context triple: [Oslo Metro Line 1, passesThrough, Brynseng]
-
A.
Bremsnes
Bremsnes is a village on the island of Averøya in Møre og Romsdal county, Norway, known for its coastal setting and local church.
-
B.
Bruhagen
Bruhagen is a village and administrative center located on the island of Averøya in Møre og Romsdal county, Norway.
-
C.
Sørenga
Sørenga is a modern waterfront neighborhood in Oslo, Norway, known for its residential developments, seaside promenade, and popular public seawater pool and beach.
-
D.
Gangstad
Gangstad is a small settlement located within the municipality of Inderøy in Trøndelag county, Norway.
-
E.
Tynset
Tynset is a rural municipality in Innlandet county, Norway, known for its vast mountain landscapes, agriculture, and role as a regional service center in the Østerdalen valley.
- 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: Brynseng Triple: [Oslo Metro Line 1, passesThrough, Brynseng]
Generated description
Brynseng is a neighborhood and transport hub in Oslo, Norway, served by the Oslo Metro and other public transit connections.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Brynseng Target entity description: Brynseng is a neighborhood and transport hub in Oslo, Norway, served by the Oslo Metro and other public transit connections.
-
A.
Bremsnes
Bremsnes is a village on the island of Averøya in Møre og Romsdal county, Norway, known for its coastal setting and local church.
-
B.
Bruhagen
Bruhagen is a village and administrative center located on the island of Averøya in Møre og Romsdal county, Norway.
-
C.
Sørenga
Sørenga is a modern waterfront neighborhood in Oslo, Norway, known for its residential developments, seaside promenade, and popular public seawater pool and beach.
-
D.
Gangstad
Gangstad is a small settlement located within the municipality of Inderøy in Trøndelag county, Norway.
-
E.
Tynset
Tynset is a rural municipality in Innlandet county, Norway, known for its vast mountain landscapes, agriculture, and role as a regional service center in the Østerdalen valley.
- 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_69c68a5f04188190ac266569c9280347 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f2279de4819081b8876d02f55388 |
completed | March 27, 2026, 9:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c810fcb1408190a6ed22213bd7830b |
completed | March 28, 2026, 5:33 p.m. |
| NEDg | Description generation | batch_69c8119611148190ae72e52242798dfe |
completed | March 28, 2026, 5:36 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8121126e08190a95ab570569158cf |
completed | March 28, 2026, 5:38 p.m. |
Created at: March 27, 2026, 3:09 p.m.