Triple
T26408569
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NIST |
E663895
|
entity |
| Predicate | AESCandidateCount |
P53820
|
FINISHED |
| Object | 15 initial candidates |
—
|
LITERAL 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: 15 initial candidates | Statement: [NIST, AESCandidateCount, 15 initial candidates]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: AESCandidateCount Context triple: [NIST, AESCandidateCount, 15 initial candidates]
-
A.
numberOfCounts
Indicates the total quantity or tally of discrete occurrences, items, or instances associated with an entity or event.
-
B.
areCountedBy
Indicates that one entity serves as the counting mechanism, record, or process by which the quantity of another entity is determined.
-
C.
numberOfCandidates
chosen
Indicates the total count of candidates associated with a given entity or context.
-
D.
numberOfApses
Indicates the quantity of apses associated with a given structure or entity.
-
E.
evaCount
Indicates a relationship where a specific count or number is associated with an evaluation-related event, action, or occurrence.
- F. None of above.
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_69ee883931888190901be96d75ee23cc |
completed | April 26, 2026, 9:48 p.m. |
| NER | Named-entity recognition | batch_69f610faa77081908956b6e8b5b1570c |
completed | May 2, 2026, 2:58 p.m. |
| PD | Predicate disambiguation | batch_69f5f800fa9c8190aab0962669fde8ac |
completed | May 2, 2026, 1:11 p.m. |
Created at: April 26, 2026, 11:36 p.m.