Triple
T14601535
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Løten |
E342715
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Åmot |
E1061048
|
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: Åmot | Statement: [Løten, borderedBy, Åmot]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Åmot Context triple: [Løten, borderedBy, Åmot]
-
A.
Åmot
Åmot is a rural municipality in Innlandet county in eastern Norway, known for its forests, rivers, and outdoor recreation.
-
B.
Åmot
Åmot is a small village in Vinje Municipality in Vestfold og Telemark county, Norway, known for its rural setting in the mountainous Telemark region.
-
C.
Åmot municipality
chosen
Åmot municipality is a rural municipality in Innlandet county, Norway, known for its vast forests, rivers, and outdoor recreation opportunities.
-
D.
Åmål
Åmål is a small town in western Sweden known for its picturesque lakeside setting and as the backdrop of the film "Show Me Love" (Fucking Åmål).
-
E.
Andrott
Andrott is a major inhabited island and urban center in the Lakshadweep archipelago of India, known for its dense population and traditional coastal communities.
- 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_69d822dec68081908c2553145c4051dc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb438748081908020ce04b869866a |
completed | April 14, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd94cc9fbc819090ae4efe9bc618aa |
completed | May 8, 2026, 7:46 a.m. |
Created at: April 10, 2026, 1:25 a.m.