Triple
T34710906
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Summer Wishes, Winter Dreams |
E1000641
|
entity |
| Predicate | bestSupportingActressNominee |
P31998
|
FINISHED |
| Object | Sylvia Sidney |
—
|
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: Sylvia Sidney | Statement: [Summer Wishes, Winter Dreams, bestSupportingActressNominee, Sylvia Sidney]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bestSupportingActressNominee Context triple: [Summer Wishes, Winter Dreams, bestSupportingActressNominee, Sylvia Sidney]
-
A.
bestSupportingActressWinner
Indicates that the subject is the person who won the Best Supporting Actress award for the specified work, year, or event.
-
B.
bestSupportingActressFilm
Indicates the film for which a person received or was nominated for a Best Supporting Actress award.
-
C.
supportingActressNominee
chosen
Indicates that a person has been nominated for an award in the category of supporting actress.
-
D.
bestSupportingActressSeriesMiniseriesOrTelevisionFilmWork
Indicates that a person received the Best Supporting Actress award for a role in a television series, miniseries, or television film for a specific work.
-
E.
bestSupportingActorWinner
Indicates that an entity has received the award for Best Supporting Actor for a particular work or event.
- 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_69f76dad3f108190a280fd0a2f4ee89a |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69f77ffa6b68819090257fed3802c239 |
completed | May 3, 2026, 5:03 p.m. |
| PD | Predicate disambiguation | batch_69f7795978c481909e152cd1bd02dd07 |
completed | May 3, 2026, 4:35 p.m. |
Created at: May 3, 2026, 3:59 p.m.