Triple

T214304
Position Surface form Disambiguated ID Type / Status
Subject MARC standards E4784 entity
Predicate hasComponent P35 FINISHED
Object NORMARC
NORMARC is a Norwegian implementation of the MARC bibliographic metadata standard used by libraries to catalog and exchange information about their collections.
 E27525 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: NORMARC | Statement: [MARC standards, hasComponent, NORMARC]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: NORMARC
Context triple: [MARC standards, hasComponent, NORMARC]
  • A. KORMARC
    KORMARC is the Korean implementation of the MARC bibliographic data format standard used for cataloging and exchanging library records in Korea.
  • B. Norman
    Norman is a masculine given name of English origin that became widely used in the English-speaking world.
  • C. NAM
    NAM is the commonly used acronym for the National Academy of Medicine, a leading U.S. nonprofit institution that provides expert advice on health, medicine, and biomedical science.
  • D. MARC
    MARC is a commuter rail service in Maryland that connects Washington, D.C. with Baltimore and other regional destinations.
  • E. Milhous
    Milhous is the distinctive middle name of Richard Nixon, the 37th president of the United States.
  • 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: NORMARC
Triple: [MARC standards, hasComponent, NORMARC]
Generated description
NORMARC is a Norwegian implementation of the MARC bibliographic metadata standard used by libraries to catalog and exchange information about their collections.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: NORMARC
Target entity description: NORMARC is a Norwegian implementation of the MARC bibliographic metadata standard used by libraries to catalog and exchange information about their collections.
  • A. KORMARC
    KORMARC is the Korean implementation of the MARC bibliographic data format standard used for cataloging and exchanging library records in Korea.
  • B. Norman
    Norman is a masculine given name of English origin that became widely used in the English-speaking world.
  • C. NAM
    NAM is the commonly used acronym for the National Academy of Medicine, a leading U.S. nonprofit institution that provides expert advice on health, medicine, and biomedical science.
  • D. MARC
    MARC is a commuter rail service in Maryland that connects Washington, D.C. with Baltimore and other regional destinations.
  • E. Milhous
    Milhous is the distinctive middle name of Richard Nixon, the 37th president of the United States.
  • 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_69a2575cb1dc8190a01ad332426dc339 completed Feb. 28, 2026, 2:47 a.m.
NER Named-entity recognition batch_69a25c32ae208190a03d504ef43ea659 completed Feb. 28, 2026, 3:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69a33e3f66888190a05ddcb0af4d3c5f completed Feb. 28, 2026, 7:13 p.m.
NEDg Description generation batch_69a33ee88af08190bfe81fba01856829 completed Feb. 28, 2026, 7:15 p.m.
NED2 Entity disambiguation (via description) batch_69a34295afa88190aae0d75a337967e3 completed Feb. 28, 2026, 7:31 p.m.
Created at: Feb. 28, 2026, 2:52 a.m.