Triple
T12368383
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ACM Transactions on Architecture and Code Optimization |
E294930
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object | TACO |
E294930
|
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: 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.
TACO
chosen
TACO is a peer-reviewed scientific journal published by the ACM that focuses on research in computer architecture and code optimization.
-
B.
Taco
Taco is a musical artist known for collaborating with the act Goblin.
-
C.
Rusty Taco
Rusty Taco is a fast-casual restaurant chain known for its street-style tacos and laid-back atmosphere.
-
D.
Tostitos
Tostitos is a popular American brand of tortilla chips and related snack foods produced by Frito-Lay.
-
E.
Trejo's Tacos
Trejo's Tacos is a Los Angeles-based Mexican restaurant chain founded by actor Danny Trejo, known for its street-style tacos and bold, casual dining vibe.
- 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_69d6ab6d8a4081908636601e69ddf262 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93fa65a608190a1597a49751185a8 |
completed | April 10, 2026, 6:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f62abdad1c8190b083791d60138f2a |
completed | May 2, 2026, 4:47 p.m. |
Created at: April 8, 2026, 9:54 p.m.