Triple
T18016185
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CelebA |
E431002
|
entity |
| Predicate | hasDataSplit |
P117562
|
FINISHED |
| Object | training set |
—
|
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: training set | Statement: [CelebA, hasDataSplit, training set]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDataSplit Context triple: [CelebA, hasDataSplit, training set]
-
A.
hasSplit
Indicates that an entity has undergone a division into two or more distinct parts, groups, or components.
-
B.
dataSplit
chosen
Indicates that a dataset is partitioned into distinct subsets (such as training, validation, or test sets) for separate roles in processing or evaluation.
-
C.
canSplit
Indicates that an entity has the ability or is allowed to divide something into two or more parts.
-
D.
hasDataFile
Indicates that an entity is associated with or linked to a specific data file that stores its related information.
-
E.
hasBrandSplitContext
Indicates that an entity’s brand is interpreted or managed differently depending on a specific contextual setting or scenario.
- 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_69d8b904530081908bf341d842464856 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4b523f588819097389e067dda7f23 |
completed | April 19, 2026, 10:57 a.m. |
| PD | Predicate disambiguation | batch_69e3f904b8048190add43883cd7cb191 |
completed | April 18, 2026, 9:35 p.m. |
Created at: April 10, 2026, 10:24 a.m.