Triple

T19410742
Position Surface form Disambiguated ID Type / Status
Subject Miss Granny E485578 entity
Predicate boxOfficeAdmissions P135769 FINISHED
Object over 8,600,000 admissions in South Korea LITERAL FINISHED

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: over 8,600,000 admissions in South Korea | Statement: [Miss Granny, boxOfficeAdmissions, over 8,600,000 admissions in South Korea]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: boxOfficeAdmissions
Context triple: [Miss Granny, boxOfficeAdmissions, over 8,600,000 admissions in South Korea]
  • A. boxOfficeStatus
    Indicates the commercial performance or financial success status of a film or media release at the box office.
  • B. admissionFee
    Indicates the monetary charge required for entry or participation in a place, event, or activity.
  • C. theaterEligibility
    Indicates whether an entity meets the required conditions to be allowed to participate in or attend a theater-related activity or event.
  • D. hasBoxOfficeType
    Indicates the classification of a work’s box office performance or revenue category (e.g., type or scale of its box office results).
  • E. cinemaStatus
    Indicates the current operational or functional state of a cinema, such as whether it is open, closed, or otherwise restricted.
  • 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_69d8e8d5162481909db12435d9535c1a completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e62af4cc0c81909056b5e2ee574ab1 completed April 20, 2026, 1:32 p.m.
PD Predicate disambiguation batch_69e4fd68b1f881908d273de1fee81a75 completed April 19, 2026, 4:06 p.m.
PDg Predicate description generation batch_69e5004c23308190a087b7941a90725f completed April 19, 2026, 4:18 p.m.
Created at: April 10, 2026, 1:37 p.m.