Triple
T18313006
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Norra Bantorget |
E438677
|
entity |
| Predicate | hasNearbyBuilding |
P5648
|
FINISHED |
| Object |
LO-borgen
LO-borgen is a historic brick headquarters of the Swedish Trade Union Confederation (LO) in central Stockholm, known as a key landmark of the Swedish labor movement.
|
E1318046
|
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: LO-borgen | Statement: [Norra Bantorget, hasNearbyBuilding, LO-borgen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LO-borgen Context triple: [Norra Bantorget, hasNearbyBuilding, LO-borgen]
-
A.
Lodalen
Lodalen is a small valley and residential-industrial area in Oslo, Norway, situated near the inner-city districts and railway facilities.
-
B.
Løkken
Løkken is a Danish seaside town known for its sandy beaches, coastal dunes, and popular summer tourism on the North Sea.
-
C.
Kulbanekvarteret
Kulbanekvarteret is a residential neighborhood in the Valby district of Copenhagen, Denmark, known for its mix of housing and urban redevelopment.
-
D.
Blokhus
Blokhus is a Danish seaside resort town known for its wide sandy beaches, coastal dunes, and tourism along the North Sea.
-
E.
Løren
Løren is a rapidly developing residential and commercial neighborhood in Oslo, Norway, known for its modern housing, new infrastructure, and proximity to central city areas.
- 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: LO-borgen Triple: [Norra Bantorget, hasNearbyBuilding, LO-borgen]
Generated description
LO-borgen is a historic brick headquarters of the Swedish Trade Union Confederation (LO) in central Stockholm, known as a key landmark of the Swedish labor movement.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: LO-borgen Target entity description: LO-borgen is a historic brick headquarters of the Swedish Trade Union Confederation (LO) in central Stockholm, known as a key landmark of the Swedish labor movement.
-
A.
Lodalen
Lodalen is a small valley and residential-industrial area in Oslo, Norway, situated near the inner-city districts and railway facilities.
-
B.
Løkken
Løkken is a Danish seaside town known for its sandy beaches, coastal dunes, and popular summer tourism on the North Sea.
-
C.
Kulbanekvarteret
Kulbanekvarteret is a residential neighborhood in the Valby district of Copenhagen, Denmark, known for its mix of housing and urban redevelopment.
-
D.
Blokhus
Blokhus is a Danish seaside resort town known for its wide sandy beaches, coastal dunes, and tourism along the North Sea.
-
E.
Løren
Løren is a rapidly developing residential and commercial neighborhood in Oslo, Norway, known for its modern housing, new infrastructure, and proximity to central city areas.
- 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_69d8b916a2d081909e249e4902f6aad9 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e5021a96b48190976273be3ff3e5c6 |
completed | April 19, 2026, 4:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a03c4bf498c8190ba7ba700ab643c9a |
completed | May 13, 2026, 12:24 a.m. |
| NEDg | Description generation | batch_6a03c592f7d0819082d600901da2c2f6 |
completed | May 13, 2026, 12:28 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a03c63ec8c48190bad47d423ab2cfe7 |
completed | May 13, 2026, 12:30 a.m. |
Created at: April 10, 2026, 10:36 a.m.