Triple
T13549420
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nicole Kidman as Sue Brierley |
E323601
|
entity |
| Predicate | portrayalEmotion |
P75115
|
FINISHED |
| Object | compassion |
—
|
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: compassion | Statement: [Nicole Kidman as Sue Brierley, portrayalEmotion, compassion]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portrayalEmotion Context triple: [Nicole Kidman as Sue Brierley, portrayalEmotion, compassion]
-
A.
emotionDisplayed
chosen
Indicates that an entity is outwardly expressing or showing a particular emotion.
-
B.
portrayalFeature
Indicates that one entity serves as a characteristic, aspect, or attribute highlighted in the depiction or representation of another entity.
-
C.
portrayalLanguage
Indicates the language in which something is depicted, represented, or expressed.
-
D.
portrayalRecognition
Indicates that one entity recognizes or identifies another entity as a portrayal or representation of a particular subject or character.
-
E.
intendedEmotion
Indicates the emotion that an action, expression, or communication is meant to evoke in its target, regardless of the actual emotion experienced.
- 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_69d8076776248190bdf0d4fa1f85a5fc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbbb9ee3f081909056dc1a92c40b7a |
completed | April 12, 2026, 3:34 p.m. |
| PD | Predicate disambiguation | batch_69dbae13bec4819084c1770638c00ed9 |
completed | April 12, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:46 p.m.