Triple

T381016
Position Surface form Disambiguated ID Type / Status
Subject NORD series E8678 entity
Predicate developer P73 FINISHED
Object Norsk Data E1928 NE FINISHED

How this triple was built (2 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: Norsk Data | Statement: [NORD series, developer, Norsk Data]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Norsk Data
Context triple: [NORD series, developer, Norsk Data]
  • A. Norsk Data NORD-10 chosen
    Norsk Data NORD-10 was a 16-bit minicomputer series from the Norwegian company Norsk Data, widely used in the 1970s and 1980s for scientific, technical, and commercial applications.
  • B. Tokyo Tsushin Kogyo
    Tokyo Tsushin Kogyo was the original name of the Japanese electronics company that later became globally known as Sony.
  • C. Hewlett-Packard
    Hewlett-Packard is a pioneering American technology company known for its innovations in computing, printers, and enterprise IT solutions.
  • D. Micros Systems
    Micros Systems was a leading provider of point-of-sale and hospitality management software and hardware solutions for restaurants, hotels, and retail businesses.
  • E. Thomson SA
    Thomson SA was a major French electronics and media conglomerate known for its consumer electronics, broadcasting, and defense-related technologies.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69a2e7f47dd08190a4e294ccbbe46cd4 completed Feb. 28, 2026, 1:04 p.m.
NER Named-entity recognition batch_69a2ec2c95088190a603bb1ee076ebd6 completed Feb. 28, 2026, 1:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3faffa5848190b77503516f3d0ba6 completed March 1, 2026, 8:38 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.