Triple

T381037
Position Surface form Disambiguated ID Type / Status
Subject NORD series E8678 entity
Predicate notableModel P1503 FINISHED
Object NORD-50
NORD-50 is a notable model within the NORD series, recognized as a representative example of that product line.
 E8678 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: NORD-50 | Statement: [NORD series, notableModel, NORD-50]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: NORD-50
Context triple: [NORD series, notableModel, NORD-50]
  • A. NORD series
    The NORD series is a line of minicomputers developed by Norwegian company Norsk Data, notable in the 1970s and 1980s for their use in scientific, technical, and real-time computing applications.
  • B. Nilsen
    Nilsen is a surname, primarily of Scandinavian origin, that serves as a variant spelling of Nelson.
  • C. Scandinavium
    Scandinavium is a major indoor arena in Gothenburg, Sweden, best known for hosting top-level ice hockey, concerts, and international sporting events.
  • D. Helleren
    Helleren is a small historic settlement in Norway known for its traditional houses built under a large rock overhang near the Jøssingfjord.
  • E. Norsk Data NORD-10
    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.
  • 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: NORD-50
Triple: [NORD series, notableModel, NORD-50]
Generated description
NORD-50 is a notable model within the NORD series, recognized as a representative example of that product line.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: NORD-50
Target entity description: NORD-50 is a notable model within the NORD series, recognized as a representative example of that product line.
  • A. NORD series chosen
    The NORD series is a line of minicomputers developed by Norwegian company Norsk Data, notable in the 1970s and 1980s for their use in scientific, technical, and real-time computing applications.
  • B. Nilsen
    Nilsen is a surname, primarily of Scandinavian origin, that serves as a variant spelling of Nelson.
  • C. Scandinavium
    Scandinavium is a major indoor arena in Gothenburg, Sweden, best known for hosting top-level ice hockey, concerts, and international sporting events.
  • D. Helleren
    Helleren is a small historic settlement in Norway known for its traditional houses built under a large rock overhang near the Jøssingfjord.
  • E. Norsk Data NORD-10
    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.
  • F. None of above.

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_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_69a405f431d48190b83e2eaa2fe0e587 completed March 1, 2026, 9:25 a.m.
NEDg Description generation batch_69a4068c6b708190b8f06efedc6ba2c6 completed March 1, 2026, 9:27 a.m.
NED2 Entity disambiguation (via description) batch_69a406d581888190a0380f362a844089 completed March 1, 2026, 9:28 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.