Triple
T21954124
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Royal Drawing School in Kristiania |
E542140
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Kristiania |
—
|
NE NERFINISHED |
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: Kristiania | Statement: [Royal Drawing School in Kristiania, locatedIn, Kristiania]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kristiania Context triple: [Royal Drawing School in Kristiania, locatedIn, Kristiania]
-
A.
Hanøya
Hanøya is a small Norwegian island that is part of the Askøy municipality in Vestland county.
-
B.
Oslo
chosen
Oslo is the capital and largest city of Norway, known as a major cultural, economic, and governmental center.
-
C.
Oslo
Oslo is a collection of shared libraries that provide common code and patterns used across various OpenStack projects.
-
D.
Copenhagen
Copenhagen is the capital and largest city of Denmark, known for its historic architecture, vibrant cultural scene, and high quality of life.
-
E.
Copenhagen
Copenhagen is a popular American smokeless tobacco (chewing tobacco/dip) brand known for its long history and strong presence in the U.S. market.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c47ef0e48190a50e1bcc43f4b3fd |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f1243dfb4081909bc7a722843ffea7 |
completed | April 28, 2026, 9:18 p.m. |
Created at: April 16, 2026, 7:59 p.m.