Triple
T1271475
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Academy Award for Best Makeup and Hairstyling |
E15718
|
entity |
| Predicate | hasSubfieldsRecognized |
P21666
|
FINISHED |
| Object | prosthetic makeup |
—
|
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: prosthetic makeup | Statement: [Academy Award for Best Makeup and Hairstyling, hasSubfieldsRecognized, prosthetic makeup]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSubfieldsRecognized Context triple: [Academy Award for Best Makeup and Hairstyling, hasSubfieldsRecognized, prosthetic makeup]
-
A.
hasSubfieldGroup
Indicates that an entity includes or is associated with a specific subgroup of related subfields within its overall structure or domain.
-
B.
hasSubLanguage
Indicates that one language is a subset, variant, or specialized form of another language.
-
C.
hasSubcomponent
Indicates that one entity is a constituent part or component of another, larger entity.
-
D.
hasSubConcept
chosen
Indicates that one concept is a more specific, subordinate, or narrower idea within the scope of another, more general concept.
-
E.
hasSubstyle
Indicates that one style is a more specific or subordinate variant of another style within a hierarchical style structure.
- 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_69a4935a94308190bb92555b79032824 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4c06ae7b88190a1e0b5232d84a7b1 |
completed | March 1, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69a4bede52a081909665d60acbe41d31 |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:50 p.m.