Triple

T6023105
Position Surface form Disambiguated ID Type / Status
Subject Peter Naur E134109 entity
Predicate employer P7 FINISHED
Object Regnecentralen
Regnecentralen was a pioneering Danish computer company and research institution known for its early contributions to computer science and computing technology in Denmark.
 E563541 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: Regnecentralen | Statement: [Peter Naur, employer, Regnecentralen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Regnecentralen
Context triple: [Peter Naur, employer, Regnecentralen]
  • A. Kjernen
    Kjernen is the main organized supporters' group of Norwegian football club Rosenborg BK, known for its passionate fan culture and vocal backing at matches.
  • B. Runhällen
    Runhällen is a small locality in central Sweden situated within Heby Municipality in Uppsala County.
  • C. Syvota
    Syvota is a coastal village and popular tourist resort on the Ionian Sea in northwestern Greece, known for its scenic bays and nearby islands.
  • D. Sentrum
    Sentrum is the central district of Oslo, Norway, which hosts some of the University of Oslo’s urban campus facilities.
  • E. Krolloper
    Krolloper was a historic Berlin theater and opera house known for its innovative productions and its role as the meeting place of the Reichstag during the Weimar Republic.
  • 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: Regnecentralen
Triple: [Peter Naur, employer, Regnecentralen]
Generated description
Regnecentralen was a pioneering Danish computer company and research institution known for its early contributions to computer science and computing technology in Denmark.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Regnecentralen
Target entity description: Regnecentralen was a pioneering Danish computer company and research institution known for its early contributions to computer science and computing technology in Denmark.
  • A. Kjernen
    Kjernen is the main organized supporters' group of Norwegian football club Rosenborg BK, known for its passionate fan culture and vocal backing at matches.
  • B. Runhällen
    Runhällen is a small locality in central Sweden situated within Heby Municipality in Uppsala County.
  • C. Syvota
    Syvota is a coastal village and popular tourist resort on the Ionian Sea in northwestern Greece, known for its scenic bays and nearby islands.
  • D. Sentrum
    Sentrum is the central district of Oslo, Norway, which hosts some of the University of Oslo’s urban campus facilities.
  • E. Krolloper
    Krolloper was a historic Berlin theater and opera house known for its innovative productions and its role as the meeting place of the Reichstag during the Weimar Republic.
  • 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_69c008742a5c8190b9cb9c2787a3d8b3 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c04fbd7978819085d683578bc62aa3 completed March 22, 2026, 8:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69c11371ceb88190b0c2d4218ed0327a completed March 23, 2026, 10:18 a.m.
NEDg Description generation batch_69c11400ddf08190b99943ada6ff2703 completed March 23, 2026, 10:20 a.m.
NED2 Entity disambiguation (via description) batch_69c1147e55fc81909225e1fe9e3eeec4 completed March 23, 2026, 10:22 a.m.
Created at: March 22, 2026, 4:07 p.m.