Triple
T25158719
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 19th Academy Awards |
E626382
|
entity |
| Predicate | bestLiveActionShortOneReelWinner |
P157972
|
FINISHED |
| Object | Facing Your Danger |
—
|
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: Facing Your Danger | Statement: [19th Academy Awards, bestLiveActionShortOneReelWinner, Facing Your Danger]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bestLiveActionShortOneReelWinner Context triple: [19th Academy Awards, bestLiveActionShortOneReelWinner, Facing Your Danger]
-
A.
bestShortSubjectOneReelWinner
Indicates that the subject has won the award for best short subject in the one-reel category.
-
B.
bestLiveActionShortSubjectTwoReelWinner
Indicates that the subject is the winner of the Academy Award for Best Live Action Short Film in the two-reel category.
-
C.
bestLiveActionShortFilmWinner
Indicates that the subject is the winner of the Best Live Action Short Film award in a given year or context.
-
D.
musicVideoCameo
Indicates that one entity makes a brief or special-appearance role in the other entity’s music video.
-
E.
featuredShort
Indicates that an entity is highlighted or promoted as a primary or notable short-form item (e.g., a featured short video, story, or clip).
- F. None of above. chosen
Provenance (4 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_69e2ff2834ec8190b0872e2ec3d76023 |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f46b8b03508190ad7ef11ca65eda05 |
completed | May 1, 2026, 8:59 a.m. |
| PD | Predicate disambiguation | batch_69f44d8043b081908bbffd7f044b4f26 |
completed | May 1, 2026, 6:51 a.m. |
| PDg | Predicate description generation | batch_69f45300bd488190bb1d4160f5534ef6 |
completed | May 1, 2026, 7:15 a.m. |
Created at: April 18, 2026, 6:31 a.m.