Triple
T14008437
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cletus Klump |
E337014
|
entity |
| Predicate | usesProstheticMakeup |
P112130
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Cletus Klump, usesProstheticMakeup, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesProstheticMakeup Context triple: [Cletus Klump, usesProstheticMakeup, true]
-
A.
usesStageMakeup
Indicates that one entity applies or wears theatrical or stage makeup in relation to another entity or context.
-
B.
hasProsthesis
Indicates that an entity possesses or is equipped with an artificial substitute for a missing or impaired body part.
-
C.
makeupArtist
Indicates that one entity serves as the makeup artist for another, applying or designing cosmetic looks for that entity.
-
D.
makeupType
Indicates the specific kind or category of makeup associated with an entity.
-
E.
hasCosmetics
Indicates that one entity possesses, uses, or is associated with cosmetic products or beauty-related items in relation to another entity or context.
- F. None of above. chosen
Provenance (4 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_69d81c645c5c8190b1fd16a285a1b78a |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2ed44f90819099ad08c09c066b56 |
completed | April 14, 2026, 12:11 p.m. |
| PD | Predicate disambiguation | batch_69dd465dfbc4819090d8c61fd572d35f |
completed | April 13, 2026, 7:39 p.m. |
| PDg | Predicate description generation | batch_69de01ed2098819088ec45069f6f2609 |
completed | April 14, 2026, 8:59 a.m. |
Created at: April 9, 2026, 10:19 p.m.