Triple
T9421452
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lysaker Station |
E227160
|
entity |
| Predicate | locatedInCounty |
P40
|
FINISHED |
| Object | Viken |
E93795
|
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: Viken | Statement: [Lysaker Station, locatedInCounty, Viken]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Viken Context triple: [Lysaker Station, locatedInCounty, Viken]
-
A.
Viken
chosen
Viken is a county in southeastern Norway that includes the area around Oslo and stretches from the Swedish border to the mountainous interior.
-
B.
Kattegat
Kattegat is a shallow sea area and strait between Denmark and Sweden that forms a key maritime passage linking the North Sea with the Baltic Sea.
-
C.
Sollentuna
Sollentuna is a suburban town in Stockholm County, Sweden, known as part of the Stockholm urban area and a residential and commercial hub just north of the capital.
-
D.
Torsken
Torsken is a small coastal village and former fishing-based municipality located on the island of Senja in northern Norway.
-
E.
Ostönnen
Ostönnen is a village and district of the town of Soest in North Rhine-Westphalia, Germany.
- 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_69ca84359e7c819091148ba4b670e436 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd6c2651c48190808281779fab49df |
completed | April 1, 2026, 7:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d107c95c9481909957b99cacf5e045 |
completed | April 4, 2026, 12:44 p.m. |
Created at: March 30, 2026, 7:48 p.m.