Triple

T2752493
Position Surface form Disambiguated ID Type / Status
Subject ACM Transactions on Architecture and Code Optimization E61019 entity
Predicate abbreviation P43 FINISHED
Object TACO
TACO is a peer-reviewed scientific journal published by the ACM that focuses on research in computer architecture and code optimization.
 E294930 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: TACO | Statement: [ACM Transactions on Architecture and Code Optimization, abbreviation, TACO]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: TACO
Context triple: [ACM Transactions on Architecture and Code Optimization, abbreviation, TACO]
  • A. Tostitos
    Tostitos is a popular American brand of tortilla chips and related snack foods produced by Frito-Lay.
  • B. Delicias
    Delicias is an important agricultural and industrial city in the Mexican state of Chihuahua, known especially for its cotton and pecan production.
  • C. Nacho Libre
    Nacho Libre is a 2006 comedy film starring Jack Black as a monastery cook who becomes a masked luchador to support his orphanage.
  • D. Tejipió
    Tejipió is a neighborhood in the city of Recife, Brazil, known as part of the urban fabric of the state capital of Pernambuco.
  • E. Anna’s Taqueria
    Anna’s Taqueria is a popular Boston-area fast-casual Mexican restaurant chain known for its made-to-order burritos and tacos.
  • 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: TACO
Triple: [ACM Transactions on Architecture and Code Optimization, abbreviation, TACO]
Generated description
TACO is a peer-reviewed scientific journal published by the ACM that focuses on research in computer architecture and code optimization.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: TACO
Target entity description: TACO is a peer-reviewed scientific journal published by the ACM that focuses on research in computer architecture and code optimization.
  • A. Tostitos
    Tostitos is a popular American brand of tortilla chips and related snack foods produced by Frito-Lay.
  • B. Delicias
    Delicias is an important agricultural and industrial city in the Mexican state of Chihuahua, known especially for its cotton and pecan production.
  • C. Nacho Libre
    Nacho Libre is a 2006 comedy film starring Jack Black as a monastery cook who becomes a masked luchador to support his orphanage.
  • D. Tejipió
    Tejipió is a neighborhood in the city of Recife, Brazil, known as part of the urban fabric of the state capital of Pernambuco.
  • E. Anna’s Taqueria
    Anna’s Taqueria is a popular Boston-area fast-casual Mexican restaurant chain known for its made-to-order burritos and tacos.
  • 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_69ab4b7a85bc819094a349b84beb1f2c completed March 6, 2026, 9:47 p.m.
NER Named-entity recognition batch_69abdb6d08088190b489de15a120ba3f completed March 7, 2026, 8:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69afbbd86ac88190a4aba335ef9942e4 completed March 10, 2026, 6:36 a.m.
NEDg Description generation batch_69afbc8415388190a39d459ff7a411e4 completed March 10, 2026, 6:39 a.m.
NED2 Entity disambiguation (via description) batch_69afbcc460b88190986844c39165ef14 completed March 10, 2026, 6:40 a.m.
Created at: March 6, 2026, 9:56 p.m.