Triple
T15305304
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Karasjok |
E365882
|
entity |
| Predicate | bordersMunicipality |
P224
|
FINISHED |
| Object | Lebesby |
E382081
|
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: Lebesby | Statement: [Karasjok, bordersMunicipality, Lebesby]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lebesby Context triple: [Karasjok, bordersMunicipality, Lebesby]
-
A.
Lebesby
chosen
Lebesby is a sparsely populated coastal municipality in Troms og Finnmark county in northern Norway, known for its Arctic landscapes, fishing communities, and proximity to the Barents Sea.
-
B.
Edsbyn
Edsbyn is a small town in Gävleborg County, Sweden, known for its bandy team and role as a local industrial and service center.
-
C.
Treseburg
Treseburg is a small village in the Harz Mountains of central Germany, known as a scenic gateway for hiking and nature tourism in the surrounding Bode Valley.
-
D.
Nesseby
Nesseby is a small coastal municipality in Troms og Finnmark county in northern Norway, known for its Sámi culture and location along the Varangerfjorden.
-
E.
Mörby
Mörby is a locality in the Stockholm area of Sweden served by a station on the Roslagsbanan narrow-gauge railway line.
- 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_69d85a113ee881908e297a1d38dd79fa |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03ccef14c819099c5ebe962e7f867 |
completed | April 16, 2026, 1:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fef89d961481909be8dcc2864982c9 |
completed | May 9, 2026, 9:04 a.m. |
Created at: April 10, 2026, 3:16 a.m.