Triple
T5508537
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sognsvann |
E144503
|
entity |
| Predicate | isNear |
P350
|
FINISHED |
| Object | Frognerseteren |
E163011
|
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: Frognerseteren | Statement: [Sognsvann, isNear, Frognerseteren]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Frognerseteren Context triple: [Sognsvann, isNear, Frognerseteren]
-
A.
Frognerseteren
chosen
Frognerseteren is a hilltop area in Oslo, Norway, known for its panoramic views over the city, traditional wooden restaurant, and access to popular hiking and skiing trails.
-
B.
Skøyen
Skøyen is a neighborhood in western Oslo, Norway, known as a busy residential and commercial hub with strong public transport connections.
-
C.
St. Hanshaugen
St. Hanshaugen is a central borough of Oslo, Norway, known for its large hillside park and vibrant urban residential areas.
-
D.
Kolsås
Kolsås is a suburban area in Bærum, Norway, known as the endpoint of one of the Oslo Metro lines and for its nearby forested hill popular for hiking and climbing.
-
E.
Lyngseidet
Lyngseidet is a small coastal village in northern Norway, known for its scenic fjord and mountain surroundings on the Lyngen Peninsula.
- 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_69c008f6b5048190a09064116062cf69 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01f4a80d88190bab0056c4c78be93 |
completed | March 22, 2026, 4:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c04cb487408190af6f4f2b3a8543c1 |
completed | March 22, 2026, 8:10 p.m. |
Created at: March 22, 2026, 3:33 p.m.