Triple
T10429024
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lunner |
E245859
|
entity |
| Predicate | bordersWith |
P224
|
FINISHED |
| Object | Ringerike |
E93797
|
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: Ringerike | Statement: [Lunner, bordersWith, Ringerike]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ringerike Context triple: [Lunner, bordersWith, Ringerike]
-
A.
Ringerike
chosen
Ringerike is a historic district and municipality in southeastern Norway known for its rich Viking-age heritage and distinctive cultural traditions.
-
B.
Nissedal
Nissedal is a rural municipality in Vestfold og Telemark county, Norway, known for its forests, lakes, and outdoor recreation opportunities.
-
C.
Verdal
Verdal is a municipality in central Norway known for its agricultural landscape, industrial activity, and the historic battlefield of Stiklestad.
-
D.
Bjerkreim
Bjerkreim is a rural municipality in southwestern Norway known for its rivers, salmon fishing, and agricultural landscape.
-
E.
Gaustad
Gaustad is a district in Oslo, Norway, known for hosting major academic and research institutions, including parts of the University of Oslo campus.
- 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_69e42d406e2c8190ad27f3a5276be25f |
completed | April 19, 2026, 1:17 a.m. |
Created at: April 6, 2026, 12:13 p.m.