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.