Triple

T1057529
Position Surface form Disambiguated ID Type / Status
Subject Regjeringskvartalet E22829 entity
Predicate containsBuilding P14728 FINISHED
Object R4
R4 is a government office building in Oslo that forms part of Norway’s central Regjeringskvartalet complex.
 E121372 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: R4 | Statement: [Regjeringskvartalet, containsBuilding, R4]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: R4
Context triple: [Regjeringskvartalet, containsBuilding, R4]
  • A. R
    R is a New York City Subway service that runs along the Broadway Line in Manhattan and Queens, providing local transit through key commercial and residential areas.
  • B. R
    R is a widely used open-source programming language and environment focused on statistical computing, data analysis, and graphical visualization.
  • C. RA
    RA is a prestigious post-nominal title indicating membership as a Royal Academician of the Royal Academy of Arts in London.
  • D. RA
    RA is the commonly used abbreviation for the Royal Regiment of Artillery, a principal artillery branch of the British Army.
  • E. RM
    RM is the currency symbol that was used to denote the German Reichsmark, the former official currency of Germany from 1924 to 1948.
  • 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: R4
Triple: [Regjeringskvartalet, containsBuilding, R4]
Generated description
R4 is a government office building in Oslo that forms part of Norway’s central Regjeringskvartalet complex.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: R4
Target entity description: R4 is a government office building in Oslo that forms part of Norway’s central Regjeringskvartalet complex.
  • A. R
    R is a New York City Subway service that runs along the Broadway Line in Manhattan and Queens, providing local transit through key commercial and residential areas.
  • B. R
    R is a widely used open-source programming language and environment focused on statistical computing, data analysis, and graphical visualization.
  • C. RA
    RA is a prestigious post-nominal title indicating membership as a Royal Academician of the Royal Academy of Arts in London.
  • D. RA
    RA is the commonly used abbreviation for the Royal Regiment of Artillery, a principal artillery branch of the British Army.
  • E. RM
    RM is the currency symbol that was used to denote the German Reichsmark, the former official currency of Germany from 1924 to 1948.
  • 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_69a493dada0481909c43649f9843ea91 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b8da80dc8190b79beaf509910725 completed March 1, 2026, 10:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac3bd110ac8190b66163de42bd3034 completed March 7, 2026, 2:53 p.m.
NEDg Description generation batch_69ac3d4b32348190883244f2b8af32a0 completed March 7, 2026, 2:59 p.m.
NED2 Entity disambiguation (via description) batch_69ac3dbf5c70819084a942fc97a9b50f completed March 7, 2026, 3:01 p.m.
Created at: March 1, 2026, 7:42 p.m.