Triple
T18016135
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CelebA |
E431002
|
entity |
| Predicate | hasAttributeType |
P3586
|
FINISHED |
| Object | binary facial attributes |
—
|
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: binary facial attributes | Statement: [CelebA, hasAttributeType, binary facial attributes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAttributeType Context triple: [CelebA, hasAttributeType, binary facial attributes]
-
A.
hasPropertyType
chosen
Indicates that an entity possesses or is associated with a specific type or category of property.
-
B.
hasKeyType
Indicates that an entity possesses or is associated with a specific category or type of key.
-
C.
hasEntryType
Indicates that something is associated with a specific category or type of entry within a system or dataset.
-
D.
hasParameterType
Indicates that a parameter in a function, method, or operation is associated with a specific data type.
-
E.
hasKeyAssetType
Indicates that an entity possesses or is associated with a specific type or category of key (primary or critical) asset.
- 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.