Triple

T1285037
Position Surface form Disambiguated ID Type / Status
Subject Mexican Academy of Language E27414 entity
Predicate hasAbbreviation P43 FINISHED
Object AML
AML is the commonly used abbreviation for the Mexican Academy of Language, a scholarly institution dedicated to the study and regulation of the Spanish language in Mexico.
 E146343 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: AML | Statement: [Mexican Academy of Language, hasAbbreviation, AML]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: AML
Context triple: [Mexican Academy of Language, hasAbbreviation, AML]
  • A. AMA
    AMA is the leading professional association and lobbying group representing physicians and medical students in the United States.
  • B. ARN
    ARN is the three-letter IATA airport code for Stockholm Arlanda Airport, the main international gateway to Stockholm and one of Sweden’s busiest airports.
  • C. ABL
    ABL is the commonly used abbreviation for the Academia Brasileira de Letras, Brazil’s foremost literary and language academy.
  • D. Al
    Al is a common shortened form of given names such as Albert, Alan, or Alexander.
  • E. ARC
    ARC is the commonly used acronym for the Augmentation Research Center, a pioneering research group known for its early work on interactive computing and human–computer interaction.
  • 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: AML
Triple: [Mexican Academy of Language, hasAbbreviation, AML]
Generated description
AML is the commonly used abbreviation for the Mexican Academy of Language, a scholarly institution dedicated to the study and regulation of the Spanish language in Mexico.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: AML
Target entity description: AML is the commonly used abbreviation for the Mexican Academy of Language, a scholarly institution dedicated to the study and regulation of the Spanish language in Mexico.
  • A. AMA
    AMA is the leading professional association and lobbying group representing physicians and medical students in the United States.
  • B. ARN
    ARN is the three-letter IATA airport code for Stockholm Arlanda Airport, the main international gateway to Stockholm and one of Sweden’s busiest airports.
  • C. ABL
    ABL is the commonly used abbreviation for the Academia Brasileira de Letras, Brazil’s foremost literary and language academy.
  • D. Al
    Al is a common shortened form of given names such as Albert, Alan, or Alexander.
  • E. ARC
    ARC is the commonly used acronym for the Augmentation Research Center, a pioneering research group known for its early work on interactive computing and human–computer interaction.
  • 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_69a496d4ec448190ad653b2590c46711 completed March 1, 2026, 7:43 p.m.
NER Named-entity recognition batch_69a4c0b6dda48190a2e79084adea6ec1 completed March 1, 2026, 10:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69aca3004b648190a4148b0421699bf9 completed March 7, 2026, 10:13 p.m.
NEDg Description generation batch_69aca3a539848190a17e8bd578bc237a completed March 7, 2026, 10:16 p.m.
NED2 Entity disambiguation (via description) batch_69aca4158bbc8190bd1f5799715e3e4c completed March 7, 2026, 10:17 p.m.
Created at: March 1, 2026, 7:50 p.m.