Triple
T36491803
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | miniImageNet |
E899067
|
entity |
| Predicate | hasTotalImageCountApprox |
P130124
|
FINISHED |
| Object | 60000 |
—
|
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: 60000 | Statement: [miniImageNet, hasTotalImageCountApprox, 60000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTotalImageCountApprox Context triple: [miniImageNet, hasTotalImageCountApprox, 60000]
-
A.
hasApproximateNumberOfImages
chosen
Indicates that an entity is associated with a quantity of images that is approximate rather than an exact count.
-
B.
hasPageCountApprox
Indicates that an entity is associated with an approximate or estimated number of pages, rather than an exact page count.
-
C.
numberOfImagesReturned
Indicates the total count of images that are produced or provided as the result of a query, request, or operation.
-
D.
hasTotalNumber
Indicates that an entity is associated with a specific overall count or sum of items, elements, or units.
-
E.
hasApproximateNumberOfPieces
Indicates that an entity is associated with an estimated or non-exact count of pieces or components.
- 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_69f76e5ad4588190bdbce60c52fbb785 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fd5f29b1988190877764ef2a399c7f |
completed | May 8, 2026, 3:57 a.m. |
| PD | Predicate disambiguation | batch_69fd5e30194c819085b5ce586122ab37 |
completed | May 8, 2026, 3:53 a.m. |
Created at: May 3, 2026, 4:10 p.m.