Triple

T18301936
Position Surface form Disambiguated ID Type / Status
Subject IFLA Library Reference Model E438376 entity
Predicate hasAbbreviation P43 FINISHED
Object LRM
LRM is a conceptual model developed by the International Federation of Library Associations and Institutions to provide a unified framework for bibliographic information and library cataloging.
 E1317538 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: LRM | Statement: [IFLA Library Reference Model, hasAbbreviation, LRM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: LRM
Context triple: [IFLA Library Reference Model, hasAbbreviation, LRM]
  • A. LRD
    LRD is the official currency code for the Liberian dollar, the legal tender used in Liberia.
  • B. LRS
    LRS is the IATA airport code for Leros Municipal Airport, a small regional airport serving the Greek island of Leros in the Dodecanese.
  • C. LMRDA
    LMRDA is a U.S. federal law enacted in 1959 to regulate labor unions’ internal affairs, promote union democracy, and ensure financial transparency and accountability.
  • D. LQM
    LQM is the IATA airport code for the airport serving Puerto Leguízamo in Colombia.
  • E. RLM
    RLM was the abbreviation for the Reich Air Ministry, the government department responsible for overseeing aviation and the Luftwaffe in Nazi Germany.
  • 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: LRM
Triple: [IFLA Library Reference Model, hasAbbreviation, LRM]
Generated description
LRM is a conceptual model developed by the International Federation of Library Associations and Institutions to provide a unified framework for bibliographic information and library cataloging.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: LRM
Target entity description: LRM is a conceptual model developed by the International Federation of Library Associations and Institutions to provide a unified framework for bibliographic information and library cataloging.
  • A. LRD
    LRD is the official currency code for the Liberian dollar, the legal tender used in Liberia.
  • B. LRS
    LRS is the IATA airport code for Leros Municipal Airport, a small regional airport serving the Greek island of Leros in the Dodecanese.
  • C. LMRDA
    LMRDA is a U.S. federal law enacted in 1959 to regulate labor unions’ internal affairs, promote union democracy, and ensure financial transparency and accountability.
  • D. LQM
    LQM is the IATA airport code for the airport serving Puerto Leguízamo in Colombia.
  • E. RLM
    RLM was the abbreviation for the Reich Air Ministry, the government department responsible for overseeing aviation and the Luftwaffe in Nazi Germany.
  • 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_69d8b915e3e881909125d760c15d0c29 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e50180ac48819090e9a8f11ba10c3d completed April 19, 2026, 4:23 p.m.
NED1 Entity disambiguation (via context triple) batch_6a03bb5e1fb481908a0b98ea130eda71 completed May 12, 2026, 11:44 p.m.
NEDg Description generation batch_6a03bdb3fb3c819095192ac49e809f55 completed May 12, 2026, 11:54 p.m.
NED2 Entity disambiguation (via description) batch_6a03c193a0a08190b33d80d45f3ed0f0 completed May 13, 2026, 12:10 a.m.
Created at: April 10, 2026, 10:35 a.m.