Triple
T10428893
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flå |
E245856
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Krødsherad |
E422312
|
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: Krødsherad | Statement: [Flå, borders, Krødsherad]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Krødsherad Context triple: [Flå, borders, Krødsherad]
-
A.
Krødsherad
chosen
Krødsherad is a rural municipality in Buskerud, Norway, known for its scenic landscapes around Lake Krøderen and outdoor recreational opportunities.
-
B.
Nordingrå
Nordingrå is a small locality in Sweden’s High Coast region, known for its coastal landscapes and traditional rural communities.
-
C.
Kragerø
Kragerø is a coastal town in Norway renowned for its picturesque archipelago, historic wooden buildings, and role as a popular summer holiday destination.
-
D.
Solør
Solør is a traditional district in Eastern Norway known for its rural landscapes, forestry, and agriculture.
-
E.
Nannestad
Nannestad is a rural municipality in Viken county, Norway, known for its agricultural landscape and proximity to Oslo Airport Gardermoen.
- 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_69d381bf3dc08190bf35a2643e4e8f22 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4ea4b4b5881908ae23f8efeea482b |
completed | April 7, 2026, 11:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de54d83e44819097abf6714bd48680 |
completed | April 14, 2026, 2:53 p.m. |
Created at: April 6, 2026, 12:13 p.m.