Triple

T17051557
Position Surface form Disambiguated ID Type / Status
Subject Algorand Standard Assets E413708 entity
Predicate alsoKnownAs P39 FINISHED
Object ASA
ASA is the token standard on the Algorand blockchain used to create and manage fungible and non-fungible digital assets.
E1248467 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: ASA | Statement: [Algorand Standard Assets, alsoKnownAs, ASA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ASA
Context triple: [Algorand Standard Assets, alsoKnownAs, ASA]
  • A. ASA
    ASA is the commonly used abbreviation for the Academy of Sciences of Albania, the country’s leading scientific research and advisory institution.
  • B. ASA
    ASA is the leading professional organization in the United States dedicated to advancing the practice and profession of statistics.
  • C. ASA
    ASA is the ICAO airline designator used to identify Alaska Airlines in international aviation operations and communications.
  • D. ASA
    ASA is a standards organization that played a key role in formalizing technical specifications such as the ASCII character encoding.
  • E. ASA
    ASA is the acronym for Aeropuertos y Servicios Auxiliares, the Mexican government agency responsible for operating and managing numerous airports and providing auxiliary aviation services in Mexico.
  • 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: ASA
Triple: [Algorand Standard Assets, alsoKnownAs, ASA]
Generated description
ASA is the token standard on the Algorand blockchain used to create and manage fungible and non-fungible digital assets.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: ASA
Target entity description: ASA is the token standard on the Algorand blockchain used to create and manage fungible and non-fungible digital assets.
  • A. ASA
    ASA is a standards organization that played a key role in formalizing technical specifications such as the ASCII character encoding.
  • B. ASA
    ASA is the acronym for the Australian Space Agency, the national body responsible for coordinating Australia’s civil space activities and industry growth.
  • C. ASA
    ASA is the ICAO airline designator used to identify Alaska Airlines in international aviation operations and communications.
  • D. ASA
    ASA is the American Society of Anesthesiologists, a major professional organization representing physicians specializing in anesthesiology and perioperative medicine.
  • E. ASA
    ASA is an abbreviation commonly used to refer to the Assistant Secretary of the Army, a senior civilian official in the United States Department of the Army responsible for high-level policy and oversight.
  • 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_69d886cde3d481908d4d01ba88ba7eb7 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3daa26e84819098b41ae15618e813 completed April 18, 2026, 7:25 p.m.
NED1 Entity disambiguation (via context triple) batch_6a012343eca0819086a07511c5d22878 completed May 11, 2026, 12:31 a.m.
NEDg Description generation batch_6a012585a1548190a112f55e2d84ccac completed May 11, 2026, 12:40 a.m.
NED2 Entity disambiguation (via description) batch_6a0126536c348190b9b2eadb4969f8c2 completed May 11, 2026, 12:44 a.m.
Created at: April 10, 2026, 5:34 a.m.