Triple

T6519284
Position Surface form Disambiguated ID Type / Status
Subject Maschinelles Austauschformat für Bibliotheken E148340 entity
Predicate abbreviation P43 FINISHED
Object MAB E27526 NE FINISHED

How this triple was built (2 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: MAB | Statement: [Maschinelles Austauschformat für Bibliotheken, abbreviation, MAB]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: MAB
Context triple: [Maschinelles Austauschformat für Bibliotheken, abbreviation, MAB]
  • A. MAB chosen
    MAB is a German bibliographic data format used for cataloging and exchanging library records, closely related to and historically aligned with MARC standards.
  • B. MABBIM
    MABBIM is a regional language council that coordinates and promotes the development and standardization of the Malay language across Brunei, Indonesia, and Malaysia.
  • C. MAB Academy
    MAB Academy is the training and development arm of Malaysia Aviation Group, providing aviation-related education and professional courses for airline and aviation industry personnel.
  • D. MAD
    MAD is a museum dedicated to contemporary art and design, showcasing innovative and experimental works across various media.
  • E. MAD
    MAD is the three-letter IATA airport code for Adolfo Suárez Madrid–Barajas Airport, the main international airport serving Madrid, Spain.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69c687e68e748190baceb9298f32d3ed completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6ac11d0e481908103c4b51de9521e completed March 27, 2026, 4:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6d51af5308190928c97ceb5d5fa2d completed March 27, 2026, 7:06 p.m.
Created at: March 27, 2026, 1:44 p.m.