Triple

T205490
Position Surface form Disambiguated ID Type / Status
Subject University of Oslo E4601 entity
Predicate hasCampus P116 FINISHED
Object Sentrum
Sentrum is the central district of Oslo, Norway, which hosts some of the University of Oslo’s urban campus facilities.
 E26400 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: Sentrum | Statement: [University of Oslo, hasCampus, Sentrum]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sentrum
Context triple: [University of Oslo, hasCampus, Sentrum]
  • A. River City
    River City is a popular nickname for Sacramento, California, highlighting the city’s close connection to the nearby American and Sacramento Rivers.
  • B. River City
    River City is a popular nickname for Wuhan, a major central Chinese metropolis known for its location at the confluence of the Yangtze and Han rivers.
  • C. Skaugum
    Skaugum is the official country residence of the Norwegian royal family, located in Asker near Oslo.
  • D. Streeterville
    Streeterville is a vibrant neighborhood on Chicago’s Near North Side known for its lakefront attractions, high-rise buildings, and major cultural and tourist destinations.
  • E. Larcomar
    Larcomar is a popular cliffside shopping and entertainment center in Lima, Peru, overlooking the Pacific Ocean and known for its restaurants, boutiques, and ocean views.
  • 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: Sentrum
Triple: [University of Oslo, hasCampus, Sentrum]
Generated description
Sentrum is the central district of Oslo, Norway, which hosts some of the University of Oslo’s urban campus facilities.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sentrum
Target entity description: Sentrum is the central district of Oslo, Norway, which hosts some of the University of Oslo’s urban campus facilities.
  • A. River City
    River City is a popular nickname for Sacramento, California, highlighting the city’s close connection to the nearby American and Sacramento Rivers.
  • B. River City
    River City is a popular nickname for Wuhan, a major central Chinese metropolis known for its location at the confluence of the Yangtze and Han rivers.
  • C. Skaugum
    Skaugum is the official country residence of the Norwegian royal family, located in Asker near Oslo.
  • D. Streeterville
    Streeterville is a vibrant neighborhood on Chicago’s Near North Side known for its lakefront attractions, high-rise buildings, and major cultural and tourist destinations.
  • E. Larcomar
    Larcomar is a popular cliffside shopping and entertainment center in Lima, Peru, overlooking the Pacific Ocean and known for its restaurants, boutiques, and ocean views.
  • 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_69a25737567c81908f9c505300239181 completed Feb. 28, 2026, 2:47 a.m.
NER Named-entity recognition batch_69a25c04e42481909e957cb34dc02731 completed Feb. 28, 2026, 3:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69a32bc77734819095e6cf53a10aa3d8 completed Feb. 28, 2026, 5:54 p.m.
NEDg Description generation batch_69a32c29d99081908ed22bfb073ad5bd completed Feb. 28, 2026, 5:55 p.m.
NED2 Entity disambiguation (via description) batch_69a32cc49eb08190ad97fdd8ead436f7 completed Feb. 28, 2026, 5:58 p.m.
Created at: Feb. 28, 2026, 2:51 a.m.