Triple
T27066243
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vinci Da |
E685180
|
entity |
| Predicate | hasProfessionInPlot |
P150357
|
FINISHED |
| Object | make-up artist |
—
|
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: make-up artist | Statement: [Vinci Da, hasProfessionInPlot, make-up artist]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProfessionInPlot Context triple: [Vinci Da, hasProfessionInPlot, make-up artist]
-
A.
hasProfessionInNarrative
chosen
Indicates that an entity holds or is assigned a particular profession or occupational role within the context of a narrative or story.
-
B.
hasProfessionTrait
Indicates that an entity possesses a particular characteristic, quality, or attribute specifically related to their profession or occupational role.
-
C.
hasGivenProfession
Indicates that an entity holds or practices a specified profession or occupation.
-
D.
hasFictionalProfessionLevel
Indicates that an entity holds a fictional or imagined profession at a specified level, rank, or degree of expertise.
-
E.
portrayedProfessionOfCharacter
Indicates that one entity is the profession or occupation depicted as being held by a particular character.
- 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_69ef14835fcc81908bd737b4267ae528 |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69fd864235b481908738dbb69556bc62 |
completed | May 8, 2026, 6:44 a.m. |
| PD | Predicate disambiguation | batch_69fd8373b6bc819091c554f29ee17fec |
completed | May 8, 2026, 6:32 a.m. |
Created at: April 27, 2026, 8:25 a.m.