Triple
T32455527
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Captain Spaulding |
E829413
|
entity |
| Predicate | makeup |
P78811
|
FINISHED |
| Object | white clown face paint |
—
|
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: white clown face paint | Statement: [Captain Spaulding, makeup, white clown face paint]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: makeup Context triple: [Captain Spaulding, makeup, white clown face paint]
-
A.
makeupType
chosen
Indicates the specific kind or category of makeup associated with an entity.
-
B.
facialMakeupIndicates
Indicates that the presence, style, or characteristics of facial makeup convey or signify a particular state, role, identity, or condition of an entity.
-
C.
cosmeticCategory
Indicates that one entity is classified as belonging to a particular cosmetic or beauty product category defined by the other entity.
-
D.
usesMakeupStyle
Indicates that one entity applies or adopts the makeup style or technique associated with another entity.
-
E.
hasMakeupEffectsBy
Indicates that the makeup effects for an entity (such as a film or production) are created or supervised by a specified person or team.
- 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_69f3491df9288190afc0b23b1d6e72ce |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6c31656c88190a3829f6663ce6f69 |
completed | May 3, 2026, 3:37 a.m. |
| PD | Predicate disambiguation | batch_69f6ba700a708190ab6db62791e43774 |
completed | May 3, 2026, 3:01 a.m. |
Created at: May 1, 2026, 12:56 a.m.