Triple

T5729534
Position Surface form Disambiguated ID Type / Status
Subject Grünerløkka E126346 entity
Predicate hasNeighbourhood P4813 FINISHED
Object Sofienberg
Sofienberg is a central residential neighborhood in Oslo, Norway, known for its park, historic buildings, and proximity to the vibrant Grünerløkka district.
 E540470 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: Sofienberg | Statement: [Grünerløkka, hasNeighbourhood, Sofienberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sofienberg
Context triple: [Grünerløkka, hasNeighbourhood, Sofienberg]
  • A. Vogelthal
    Vogelthal is a small village in Bavaria, Germany, known as the birthplace of World War II tank commander Michael Wittmann.
  • B. Köstendorf
    Köstendorf is a small Austrian municipality in the state of Salzburg, known for its rural character and proximity to the city of Salzburg.
  • C. Hesselberg
    Hesselberg is a prominent hill in Bavaria, Germany, known as the highest elevation of the Franconian Alb region.
  • D. Reundorf
    Reundorf is a village-level subdivision of the town of Lichtenfels in the Upper Franconia region of Bavaria, Germany.
  • E. Seckbach
    Seckbach is a district in the east of Frankfurt am Main, Germany, known for its residential character and proximity to green spaces like the Lohrberg.
  • 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: Sofienberg
Triple: [Grünerløkka, hasNeighbourhood, Sofienberg]
Generated description
Sofienberg is a central residential neighborhood in Oslo, Norway, known for its park, historic buildings, and proximity to the vibrant Grünerløkka district.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sofienberg
Target entity description: Sofienberg is a central residential neighborhood in Oslo, Norway, known for its park, historic buildings, and proximity to the vibrant Grünerløkka district.
  • A. Vogelthal
    Vogelthal is a small village in Bavaria, Germany, known as the birthplace of World War II tank commander Michael Wittmann.
  • B. Köstendorf
    Köstendorf is a small Austrian municipality in the state of Salzburg, known for its rural character and proximity to the city of Salzburg.
  • C. Hesselberg
    Hesselberg is a prominent hill in Bavaria, Germany, known as the highest elevation of the Franconian Alb region.
  • D. Reundorf
    Reundorf is a village-level subdivision of the town of Lichtenfels in the Upper Franconia region of Bavaria, Germany.
  • E. Seckbach
    Seckbach is a district in the east of Frankfurt am Main, Germany, known for its residential character and proximity to green spaces like the Lohrberg.
  • 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_69c0082f723881908ce8bb13a0c0f8b7 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c025303860819093e51f176babed71 completed March 22, 2026, 5:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69c05a8cca748190b471c842fd2ce218 completed March 22, 2026, 9:09 p.m.
NEDg Description generation batch_69c05b7c3bd48190ad8303bf1bb3ec6a completed March 22, 2026, 9:13 p.m.
NED2 Entity disambiguation (via description) batch_69c05c22c31081909a9a67d99e7c728c completed March 22, 2026, 9:16 p.m.
Created at: March 22, 2026, 3:47 p.m.