Triple
T9421573
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stabekk |
E227163
|
entity |
| Predicate | hasNeighbourhood |
P4813
|
FINISHED |
| Object |
Nadderud
Nadderud is a residential and sports-focused area in Bærum, Norway, known for its stadium and athletic facilities.
|
E798439
|
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: Nadderud | Statement: [Stabekk, hasNeighbourhood, Nadderud]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nadderud Context triple: [Stabekk, hasNeighbourhood, Nadderud]
-
A.
Orkdal
Orkdal was a former municipality in Trøndelag county, Norway, known for its central location in the Orkdalen valley and later incorporation into the larger Orkland municipality.
-
B.
Nordingrå
Nordingrå is a small locality in Sweden’s High Coast region, known for its coastal landscapes and traditional rural communities.
-
C.
Nissedal
Nissedal is a rural municipality in Vestfold og Telemark county, Norway, known for its forests, lakes, and outdoor recreation opportunities.
-
D.
Mortensrud
Mortensrud is a residential neighborhood in the Søndre Nordstrand borough of Oslo, Norway, known for its multicultural population and modern church, and served as the terminus of an Oslo Metro line.
-
E.
Ogndal
Ogndal was a former rural municipality in Trøndelag county, Norway, that was incorporated into the town of Steinkjer.
- 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: Nadderud Triple: [Stabekk, hasNeighbourhood, Nadderud]
Generated description
Nadderud is a residential and sports-focused area in Bærum, Norway, known for its stadium and athletic facilities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nadderud Target entity description: Nadderud is a residential and sports-focused area in Bærum, Norway, known for its stadium and athletic facilities.
-
A.
Orkdal
Orkdal was a former municipality in Trøndelag county, Norway, known for its central location in the Orkdalen valley and later incorporation into the larger Orkland municipality.
-
B.
Nordingrå
Nordingrå is a small locality in Sweden’s High Coast region, known for its coastal landscapes and traditional rural communities.
-
C.
Nissedal
Nissedal is a rural municipality in Vestfold og Telemark county, Norway, known for its forests, lakes, and outdoor recreation opportunities.
-
D.
Mortensrud
Mortensrud is a residential neighborhood in the Søndre Nordstrand borough of Oslo, Norway, known for its multicultural population and modern church, and served as the terminus of an Oslo Metro line.
-
E.
Ogndal
Ogndal was a former rural municipality in Trøndelag county, Norway, that was incorporated into the town of Steinkjer.
- 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_69ca84359e7c819091148ba4b670e436 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd6c2651c48190808281779fab49df |
completed | April 1, 2026, 7:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d107c95c9481909957b99cacf5e045 |
completed | April 4, 2026, 12:44 p.m. |
| NEDg | Description generation | batch_69d1085a980c8190b4c6d811b07ab180 |
completed | April 4, 2026, 12:47 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1093f440481909aa27287019191ac |
completed | April 4, 2026, 12:51 p.m. |
Created at: March 30, 2026, 7:48 p.m.