Triple
T13728551
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bootstrap Bill Turner |
E329728
|
entity |
| Predicate | makeupEffect |
P16366
|
FINISHED |
| Object | barnacle-encrusted appearance |
—
|
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: barnacle-encrusted appearance | Statement: [Bootstrap Bill Turner, makeupEffect, barnacle-encrusted appearance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: makeupEffect Context triple: [Bootstrap Bill Turner, makeupEffect, barnacle-encrusted appearance]
-
A.
usesStageMakeup
Indicates that one entity applies or wears theatrical or stage makeup in relation to another entity or context.
-
B.
makeupType
Indicates the specific kind or category of makeup associated with an entity.
-
C.
visualEffect
chosen
Indicates that one entity produces, modifies, or is associated with a particular visual effect on another entity or within a scene.
-
D.
makeupArtist
Indicates that one entity serves as the makeup artist for another, applying or designing cosmetic looks for that entity.
-
E.
specialEffectsBy
Indicates that the special effects for something (such as a film, scene, or shot) are created or provided by a particular person or entity.
- 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_69d80772315881908f980cae40d91664 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69de01f746cc8190abde237bbb7e6c78 |
completed | April 14, 2026, 8:59 a.m. |
| PD | Predicate disambiguation | batch_69dbbe92d77c81908e0244cffb7f78c5 |
completed | April 12, 2026, 3:47 p.m. |
Created at: April 9, 2026, 9:55 p.m.