Triple
T31495603
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rose Dawson Calvert |
E803530
|
entity |
| Predicate | youngerSelfPortrayedBy |
P39940
|
FINISHED |
| Object | Kate Winslet |
—
|
NE NERFINISHED |
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: Kate Winslet | Statement: [Rose Dawson Calvert, youngerSelfPortrayedBy, Kate Winslet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: youngerSelfPortrayedBy Context triple: [Rose Dawson Calvert, youngerSelfPortrayedBy, Kate Winslet]
-
A.
olderSelfPortrayedBy
Indicates that one entity is portrayed as the older version of another entity, typically in a narrative or performance context.
-
B.
portraysYoungerVersionOfCharacterFrom
Indicates that one character is depicted as a younger version of another character from a specified source.
-
C.
youngerVersionPortrayedBy
chosen
Indicates that one person portrays a younger version of another person, typically in a film, television show, or similar narrative work.
-
D.
portraysFromAge
Indicates that one entity depicts another entity starting from a specified age of the depicted entity.
-
E.
selfImage
Indicates an entity’s perception, evaluation, or mental representation of itself.
- 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_69f348cae52081909fa8e5f697523ae3 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6a1e8f46c819085d3019e45989d9c |
completed | May 3, 2026, 1:16 a.m. |
| PD | Predicate disambiguation | batch_69f69fe82e5c81909da9db0a2f3bba6d |
completed | May 3, 2026, 1:07 a.m. |
Created at: April 30, 2026, 9:41 p.m.