Triple
T831351
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trøndelag |
E17971
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Oppdal
Oppdal is a Norwegian mountain municipality and popular ski and outdoor recreation destination in central Norway.
|
E128058
|
NE FINISHED |
How this triple was built (4 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: Oppdal | Statement: [Trøndelag, contains, Oppdal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oppdal Context triple: [Trøndelag, contains, Oppdal]
-
A.
Steinkjer
Steinkjer is a town and municipality in central Norway that serves as an important regional center and administrative hub in Trøndelag county.
-
B.
Gjøvik
Gjøvik is a town and municipality in Innlandet county, Norway, known for its location along Lake Mjøsa and its mix of industrial heritage and modern sports and cultural facilities.
-
C.
Gaustad
Gaustad is a district in Oslo, Norway, known for hosting major academic and research institutions, including parts of the University of Oslo campus.
-
D.
Skien
Skien is a historic city in southern Norway known as the birthplace of playwright Henrik Ibsen and as a regional commercial and industrial center.
-
E.
Drammen
Drammen is a city and municipality in southeastern Norway known for its riverside setting along the Drammenselva and its role as a regional commercial and transport hub.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Oppdal Triple: [Trøndelag, contains, Oppdal]
Generated description
Oppdal is a Norwegian mountain municipality and popular ski and outdoor recreation destination in central Norway.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Oppdal Target entity description: Oppdal is a Norwegian mountain municipality and popular ski and outdoor recreation destination in central Norway.
-
A.
Steinkjer
Steinkjer is a town and municipality in central Norway that serves as an important regional center and administrative hub in Trøndelag county.
-
B.
Gjøvik
Gjøvik is a town and municipality in Innlandet county, Norway, known for its location along Lake Mjøsa and its mix of industrial heritage and modern sports and cultural facilities.
-
C.
Gaustad
Gaustad is a district in Oslo, Norway, known for hosting major academic and research institutions, including parts of the University of Oslo campus.
-
D.
Skien
Skien is a historic city in southern Norway known as the birthplace of playwright Henrik Ibsen and as a regional commercial and industrial center.
-
E.
Drammen
Drammen is a city and municipality in southeastern Norway known for its riverside setting along the Drammenselva and its role as a regional commercial and transport hub.
- F. None of above. chosen
Provenance (5 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_69a4937c9c188190aaa216f6b466f452 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4abb4be948190ae757df85bdc40e4 |
completed | March 1, 2026, 9:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac53753d308190928675f60e27d702 |
completed | March 7, 2026, 4:33 p.m. |
| NEDg | Description generation | batch_69ac541f80208190bf23aad6a21515bd |
completed | March 7, 2026, 4:36 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac548b363881908de3588d34c4960c |
completed | March 7, 2026, 4:38 p.m. |
Created at: March 1, 2026, 7:38 p.m.