Triple

T214302
Position Surface form Disambiguated ID Type / Status
Subject MARC standards E4784 entity
Predicate hasComponent P35 FINISHED
Object CMARC
CMARC is the Chinese Machine-Readable Cataloging format, a localized variant of the MARC bibliographic standard used primarily in Chinese-language library cataloging systems.
 E27069 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: CMARC | Statement: [MARC standards, hasComponent, CMARC]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: CMARC
Context triple: [MARC standards, hasComponent, CMARC]
  • A. IMCO
    IMCO is the former acronym for the International Maritime Organization, the United Nations agency responsible for regulating international shipping and maritime safety.
  • B. MGA
    MGA is the commonly used abbreviation for the Maryland General Assembly, the state’s bicameral legislative body.
  • C. HMC
    HMC is a professional association of leading independent school heads in the United Kingdom and internationally.
  • D. MC
    MC is the official abbreviation for NATO’s highest military authority, the NATO Military Committee.
  • E. MC
    MC is the postnominal abbreviation used to denote recipients of the Military Cross, a British military decoration awarded for gallantry during active operations against the enemy.
  • 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: CMARC
Triple: [MARC standards, hasComponent, CMARC]
Generated description
CMARC is the Chinese Machine-Readable Cataloging format, a localized variant of the MARC bibliographic standard used primarily in Chinese-language library cataloging systems.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: CMARC
Target entity description: CMARC is the Chinese Machine-Readable Cataloging format, a localized variant of the MARC bibliographic standard used primarily in Chinese-language library cataloging systems.
  • A. IMCO
    IMCO is the former acronym for the International Maritime Organization, the United Nations agency responsible for regulating international shipping and maritime safety.
  • B. MGA
    MGA is the commonly used abbreviation for the Maryland General Assembly, the state’s bicameral legislative body.
  • C. HMC
    HMC is a professional association of leading independent school heads in the United Kingdom and internationally.
  • D. MC
    MC is the official abbreviation for NATO’s highest military authority, the NATO Military Committee.
  • E. MC
    MC is the postnominal abbreviation used to denote recipients of the Military Cross, a British military decoration awarded for gallantry during active operations against the enemy.
  • 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_69a338e980908190871a2236375de6bd completed Feb. 28, 2026, 6:50 p.m.
NEDg Description generation batch_69a339380100819084982498f6b7161e completed Feb. 28, 2026, 6:51 p.m.
NED2 Entity disambiguation (via description) batch_69a33959072c819087e699c8a583e020 completed Feb. 28, 2026, 6:52 p.m.
Created at: Feb. 28, 2026, 2:52 a.m.