Triple
T1047183
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Munch Museum |
E22607
|
entity |
| Predicate | previousLocation |
P13839
|
FINISHED |
| Object | Tøyen, Oslo |
E30974
|
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: Tøyen, Oslo | Statement: [Munch Museum, previousLocation, Tøyen, Oslo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tøyen, Oslo Context triple: [Munch Museum, previousLocation, Tøyen, Oslo]
-
A.
Tøyen
chosen
Tøyen is a neighborhood in Oslo, Norway, known for its cultural institutions, parks, and educational facilities.
-
B.
Trondheim
Trondheim is a historic Norwegian city in Trøndelag county, known for its medieval Nidaros Cathedral and role as a former capital of Norway.
-
C.
Oslo
Oslo is the capital and largest city of Norway, known as a major cultural, economic, and governmental center.
-
D.
Porsgrunn
Porsgrunn is an industrial and port city in Telemark county in southeastern Norway, known for its porcelain production and location along the Telemark Canal.
-
E.
Bergen
Bergen is Norway's second-largest city, renowned for its historic harbor, surrounding mountains and fjords, and role as a former Hanseatic trading hub.
- 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_69a493da02e081908c13ff5e02a0fe7a |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b84d30888190b66f7245d781957d |
completed | March 1, 2026, 10:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac8f675824819091de0a83b2a6a0b3 |
completed | March 7, 2026, 8:49 p.m. |
Created at: March 1, 2026, 7:42 p.m.