Triple
T10428129
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hole |
E245838
|
entity |
| Predicate | previousCounty |
P65436
|
FINISHED |
| Object | Buskerud |
E95618
|
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: Buskerud | Statement: [Hole, previousCounty, Buskerud]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Buskerud Context triple: [Hole, previousCounty, Buskerud]
-
A.
Buskerud
chosen
Buskerud is a former county in southeastern Norway known for its varied landscape of forests, rivers, and mountains, including parts of the Hallingdal valley and Hardangervidda plateau.
-
B.
Hedmark
Hedmark is a former county in eastern Norway known for its vast forests, agriculture, and inland landscapes along the Swedish border.
-
C.
Hedmarken
Hedmarken is a traditional district in Innlandet county in eastern Norway, known for its agricultural landscapes and its central town, Hamar.
-
D.
Agder
Agder is a county in southern Norway known for its long coastline, maritime heritage, and popular coastal towns and islands.
-
E.
Sogn og Fjordane
Sogn og Fjordane was a former county in western Norway known for its dramatic fjords, mountains, and coastal landscapes.
- 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_69d4ea4a7dcc81909a830e08656a1c0c |
completed | April 7, 2026, 11:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d90d92510481909135a75b2f582795 |
completed | April 10, 2026, 2:47 p.m. |
Created at: April 6, 2026, 12:13 p.m.