Triple
T8609770
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ANEP |
E203887
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object | ANEP |
E203887
|
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: ANEP | Statement: [ANEP, abbreviation, ANEP]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ANEP Context triple: [ANEP, abbreviation, ANEP]
-
A.
ANEP
chosen
ANEP is Uruguay’s National Administration of Public Education, the autonomous body responsible for overseeing and managing the country’s public education system.
-
B.
AEP
AEP is a commonly used abbreviation for the Medicare Annual Enrollment Period, the yearly window when beneficiaries can change their Medicare coverage.
-
C.
AEP
AEP is the IATA airport code for Aeroparque Jorge Newbery, the main domestic and regional airport serving Buenos Aires, Argentina.
-
D.
AANES
AANES is a de facto self-governing political entity in northern and eastern Syria, often associated with Kurdish-led, multi-ethnic autonomous administration and its experiment in decentralized, democratic governance.
-
E.
ANE
ANE is Apple's dedicated on-device neural processing unit designed to accelerate machine learning tasks efficiently on Apple hardware.
- 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_69ca832c23e4819095a9f3eea4a21828 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cc46ed77588190a872d22d9d1f7429 |
completed | March 31, 2026, 10:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cea90dd93081908140ac0ce23be820 |
completed | April 2, 2026, 5:36 p.m. |
Created at: March 30, 2026, 6:25 p.m.