Triple
T8446125
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prince Prospero |
E199679
|
entity |
| Predicate | reactionToMaskedFigure |
P13275
|
FINISHED |
| Object | anger |
—
|
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: anger | Statement: [Prince Prospero, reactionToMaskedFigure, anger]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: reactionToMaskedFigure Context triple: [Prince Prospero, reactionToMaskedFigure, anger]
-
A.
faceExpression
Indicates the specific facial expression an entity is displaying, capturing its visible emotional or expressive state.
-
B.
defacedWith
Indicates that one entity has been damaged, marred, or vandalized using another entity as the means or material of defacement.
-
C.
portrayalRecognition
Indicates that one entity recognizes or identifies another entity as a portrayal or representation of a particular subject or character.
-
D.
avatar
Indicates that one entity serves as a representation, embodiment, or proxy of another entity, often in a different form or medium.
-
E.
viewOnEmotion
chosen
Indicates a relationship where one entity holds a particular emotional perspective, reaction, or attitude toward another entity or situation.
- 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_69ca83170f9081909cd98f55614c6476 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe3152a3c819092efdeab718def7a |
completed | March 31, 2026, 3:07 p.m. |
| PD | Predicate disambiguation | batch_69cbd0f5a3648190beb53a139a2d5482 |
completed | March 31, 2026, 1:49 p.m. |
Created at: March 30, 2026, 6:09 p.m.