Triple
T13549435
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nicole Kidman as Sue Brierley |
E323601
|
entity |
| Predicate | portrayalAgeRangeOfCharacter |
P94896
|
FINISHED |
| Object | middle-aged woman |
—
|
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: middle-aged woman | Statement: [Nicole Kidman as Sue Brierley, portrayalAgeRangeOfCharacter, middle-aged woman]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portrayalAgeRangeOfCharacter Context triple: [Nicole Kidman as Sue Brierley, portrayalAgeRangeOfCharacter, middle-aged woman]
-
A.
portrayedByCharacterAgeApprox
Indicates that an entity is portrayed by a character whose age is approximately a specified value or age range.
-
B.
portraysAgeGroup
Indicates that one entity depicts or represents another entity as belonging to a particular age group.
-
C.
characterAgeDescriptor
chosen
Indicates how a character’s age is qualitatively described or categorized (e.g., young, middle-aged, elderly) rather than given as a specific number.
-
D.
portrayedAsAdultBy
Indicates that one entity is depicted or represented as an adult by another entity (such as an artist, author, or creator).
-
E.
portraysYoungerVersionOfCharacterFrom
Indicates that one character is depicted as a younger version of another character from a specified source.
- 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.