Triple

T379213
Position Surface form Disambiguated ID Type / Status
Subject Model Rules of Professional Conduct E8639 entity
Predicate abbreviation P43 FINISHED
Object MRPC
MRPC is the commonly used abbreviation for the Model Rules of Professional Conduct, a set of ethical standards governing lawyers’ professional behavior in the United States.
 E48282 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: MRPC | Statement: [Model Rules of Professional Conduct, abbreviation, MRPC]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MRPC
Context triple: [Model Rules of Professional Conduct, abbreviation, MRPC]
  • A. MCRC
    MCRC is the United States Marine Corps Recruiting Command responsible for enlisting and accessing new Marines into the Corps.
  • B. GPT-2
    GPT-2 is a large transformer-based language model known for generating coherent, human-like text and sparking widespread discussion about the implications of advanced AI text generation.
  • C. MR
    MR is a Belgian French-speaking liberal political party that participated as one of the partners in the federal Vivaldi coalition government led by Alexander De Croo.
  • D. MAR
    MAR is the three-letter ISO 3166-1 alpha-3 country code assigned to Morocco.
  • E. MAB
    MAB is a German bibliographic data format used for cataloging and exchanging library records, closely related to and historically aligned with MARC standards.
  • 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: MRPC
Triple: [Model Rules of Professional Conduct, abbreviation, MRPC]
Generated description
MRPC is the commonly used abbreviation for the Model Rules of Professional Conduct, a set of ethical standards governing lawyers’ professional behavior in the United States.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MRPC
Target entity description: MRPC is the commonly used abbreviation for the Model Rules of Professional Conduct, a set of ethical standards governing lawyers’ professional behavior in the United States.
  • A. MCRC
    MCRC is the United States Marine Corps Recruiting Command responsible for enlisting and accessing new Marines into the Corps.
  • B. GPT-2
    GPT-2 is a large transformer-based language model known for generating coherent, human-like text and sparking widespread discussion about the implications of advanced AI text generation.
  • C. MR
    MR is a Belgian French-speaking liberal political party that participated as one of the partners in the federal Vivaldi coalition government led by Alexander De Croo.
  • D. MAR
    MAR is the three-letter ISO 3166-1 alpha-3 country code assigned to Morocco.
  • E. MAB
    MAB is a German bibliographic data format used for cataloging and exchanging library records, closely related to and historically aligned with MARC standards.
  • 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_69a2e7f47dd08190a4e294ccbbe46cd4 completed Feb. 28, 2026, 1:04 p.m.
NER Named-entity recognition batch_69a2ec2b07248190979229bad3a741c9 completed Feb. 28, 2026, 1:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3fafe091881908fdf8ddbb6b8a7e6 completed March 1, 2026, 8:38 a.m.
NEDg Description generation batch_69a3fba8a31881909a32dca83c07e197 completed March 1, 2026, 8:41 a.m.
NED2 Entity disambiguation (via description) batch_69a3fc96cbd88190b05e70c73cbb45c0 completed March 1, 2026, 8:45 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.