Triple
T7394572
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oslo Metro Line 1 |
E170589
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object | Tøyen |
E30974
|
NE FINISHED |
How this triple was built (2 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: Tøyen | Statement: [Oslo Metro Line 1, passesThrough, Tøyen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tøyen Context triple: [Oslo Metro Line 1, passesThrough, Tøyen]
-
A.
Tøyen
chosen
Tøyen is a neighborhood in Oslo, Norway, known for its cultural institutions, parks, and educational facilities.
-
B.
Bjerkreim
Bjerkreim is a rural municipality in southwestern Norway known for its rivers, salmon fishing, and agricultural landscape.
-
C.
Trysil
Trysil is a Norwegian municipality renowned for its large alpine ski resort and extensive outdoor recreation opportunities.
-
D.
Tingvoll
Tingvoll is a small municipality and village area in western Norway known for its rural landscape, fjords, and agricultural traditions.
-
E.
Skøyen
Skøyen is a neighborhood in western Oslo, Norway, known as a busy residential and commercial hub with strong public transport connections.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c968c9a1a48190b6ea5bb08ff745af |
completed | March 29, 2026, 6 p.m. |
Created at: March 27, 2026, 3:09 p.m.