Triple
T16737394
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brunkebergstorg |
E406751
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object |
Brunkebergsåsen
Brunkebergsåsen is a prominent esker (glacial ridge) in central Stockholm that has significantly shaped the city’s topography and urban development.
|
E1231060
|
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: Brunkebergsåsen | Statement: [Brunkebergstorg, namedAfter, Brunkebergsåsen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brunkebergsåsen Context triple: [Brunkebergstorg, namedAfter, Brunkebergsåsen]
-
A.
Aspeberget
Aspeberget is a notable rock carving site in Sweden’s Tanum World Heritage area, known for its Bronze Age petroglyphs.
-
B.
Kjerkeberget
Kjerkeberget is a forested hill in Norway that marks the highest natural point within Oslo’s municipal boundaries.
-
C.
Hemnesberget
Hemnesberget is a small coastal village in Nordland county, Norway, known for its scenic fjord-side location and role as an administrative and service center for the surrounding area.
-
D.
Skøyenåsen
Skøyenåsen is a residential neighborhood in Oslo, Norway, known for its green surroundings and access to public transportation.
-
E.
Kvernberget
Kvernberget is a hill and surrounding area in Kristiansund, Norway, known for giving its name to the nearby regional airport.
- 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: Brunkebergsåsen Triple: [Brunkebergstorg, namedAfter, Brunkebergsåsen]
Generated description
Brunkebergsåsen is a prominent esker (glacial ridge) in central Stockholm that has significantly shaped the city’s topography and urban development.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Brunkebergsåsen Target entity description: Brunkebergsåsen is a prominent esker (glacial ridge) in central Stockholm that has significantly shaped the city’s topography and urban development.
-
A.
Aspeberget
Aspeberget is a notable rock carving site in Sweden’s Tanum World Heritage area, known for its Bronze Age petroglyphs.
-
B.
Kjerkeberget
Kjerkeberget is a forested hill in Norway that marks the highest natural point within Oslo’s municipal boundaries.
-
C.
Hemnesberget
Hemnesberget is a small coastal village in Nordland county, Norway, known for its scenic fjord-side location and role as an administrative and service center for the surrounding area.
-
D.
Skøyenåsen
Skøyenåsen is a residential neighborhood in Oslo, Norway, known for its green surroundings and access to public transportation.
-
E.
Kvernberget
Kvernberget is a hill and surrounding area in Kristiansund, Norway, known for giving its name to the nearby regional airport.
- 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_69d8838ffb088190a0b11149929006bf |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e39c3b60fc81908cf331448b4b5598 |
completed | April 18, 2026, 2:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a009d4ea8208190aed0a4014a10d120 |
completed | May 10, 2026, 2:59 p.m. |
| NEDg | Description generation | batch_6a009ed297488190a20558efb9f91a55 |
completed | May 10, 2026, 3:05 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a009f460010819086cfd7a7d74cb435 |
completed | May 10, 2026, 3:07 p.m. |
Created at: April 10, 2026, 5:20 a.m.