Triple
T32128368
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 37th Academy Awards |
E820566
|
entity |
| Predicate | bestShortSubjectLiveActionSubjects |
P174977
|
FINISHED |
| Object | Another Day, Another Man |
—
|
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: Another Day, Another Man | Statement: [37th Academy Awards, bestShortSubjectLiveActionSubjects, Another Day, Another Man]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bestShortSubjectLiveActionSubjects Context triple: [37th Academy Awards, bestShortSubjectLiveActionSubjects, Another Day, Another Man]
-
A.
bestLiveActionShortSubjectTwoReelWinner
Indicates that the subject is the winner of the Academy Award for Best Live Action Short Film in the two-reel category.
-
B.
bestShortSubjectOneReelWinner
Indicates that the subject has won the award for best short subject in the one-reel category.
-
C.
bestShortSubjectCartoonWinner
Indicates that the subject has won an award for best short cartoon.
-
D.
bestShortSubjectComedyWinner
Indicates that the subject is the winner of an award for best short-form comedy.
-
E.
subjectOfFilm
Indicates that a person, character, or topic is the main focus or central topic depicted in a particular film.
- 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_69f34902d42c819083a8e6bba9a8bb9a |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6cd9bae8c8190b528641499162a75 |
completed | May 3, 2026, 4:22 a.m. |
| PD | Predicate disambiguation | batch_69f6cc1470808190b70cdfd7a6395670 |
completed | May 3, 2026, 4:16 a.m. |
| PDg | Predicate description generation | batch_69f6cd119cac8190a0b3ebe8b9c742c2 |
completed | May 3, 2026, 4:20 a.m. |
Created at: May 1, 2026, 12:29 a.m.