Triple

T32128368

Position	Surface form	Disambiguated ID	Type / Status
Subject	37th Academy Awards	`E820566`	entity
Predicate	bestShortSubjectLiveActionSubjects	`P174977`	FINISHED
Object	Another Day, Another Man "Another Day, Another Man" is a short live-action film that was nominated for Best Live Action Short Subject at the 37th Academy Awards.	`E1993050`	NE FINISHED

How this triple was built (3 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]

NEDg Description generation gpt-5.1

Instruction

Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"

Input

Entity: Another Day, Another Man
Triple: [37th Academy Awards, bestShortSubjectLiveActionSubjects, Another Day, Another Man]

Generated description

"Another Day, Another Man" is a short live-action film that was nominated for Best Live Action Short Subject at the 37th Academy Awards.

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 (7 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.
NED1	Entity disambiguation (via context triple)	`batch_6a2f013555348190900d89904e8582f9`	completed	June 14, 2026, 7:29 p.m.
NEDg	Description generation	`batch_6a2f01d797e48190bf1717ba725d7141`	completed	June 14, 2026, 7:32 p.m.
NED2	Entity disambiguation (via description)	`batch_6a2f02d3ff748190adb82f02b7629721`	completed	June 14, 2026, 7:36 p.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.