Triple

T4044804
Position Surface form Disambiguated ID Type / Status
Subject Mathematical Association of America E84038 entity
Predicate hasAbbreviation P43 FINISHED
Object MAA E84038 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: MAA | Statement: [Mathematical Association of America, hasAbbreviation, MAA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: MAA
Context triple: [Mathematical Association of America, hasAbbreviation, MAA]
  • A. MAA
    MAA is the acronym for the Maryland Aviation Administration, the state agency that oversees and manages Maryland’s public-use airports, including Baltimore/Washington International Thurgood Marshall Airport.
  • B. MAA
    MAA is the three-letter IATA airport code for Chennai International Airport, a major aviation hub in southern India.
  • C. Mathematical Association of America chosen
    The Mathematical Association of America is a professional society dedicated to the advancement, exposition, and teaching of undergraduate-level mathematics in the United States and beyond.
  • D. AMS
    AMS is the three-letter IATA airport code for Amsterdam Airport Schiphol, the main international airport of the Netherlands.
  • E. AMS
    AMS is a United States Department of Agriculture agency that develops quality standards, grading, and marketing programs for agricultural products to support fair and efficient markets.
  • 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_69aed930bd5c819083e7dcc14fc44f69 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefb5f85d48190ba80a0a24fbe438a completed March 9, 2026, 4:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5629c312c8190b89af732e2ad0fa3 completed March 14, 2026, 1:29 p.m.
Created at: March 9, 2026, 3:37 p.m.