Triple

T3896655
Position Surface form Disambiguated ID Type / Status
Subject Alfred Eisenstaedt E90386 entity
Predicate hasPhotographicStyle P27062 FINISHED
Object black-and-white photography 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: black-and-white photography | Statement: [Alfred Eisenstaedt, hasPhotographicStyle, black-and-white photography]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasPhotographicStyle
Context triple: [Alfred Eisenstaedt, hasPhotographicStyle, black-and-white photography]
  • A. hasFilmStyle
    Indicates that a film exhibits or is characterized by a particular cinematic style or aesthetic approach.
  • B. hasPhotographicConvention chosen
    Indicates that there is an established photographic style, rule, or convention governing how the related entities are visually represented in photographs.
  • C. isPhotographicSubject
    Indicates that an entity serves as the subject or main focus captured in a photograph taken by another entity.
  • D. hasPhotograph
    Indicates that one entity possesses, includes, or is associated with a photograph depicting or representing another entity.
  • E. hasPhotographicSeries
    Indicates that one entity is associated with, or is the subject of, a specific photographic series created or maintained by another entity.
  • 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_69aed95d315881908cbf1bf4a7215fbf completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aef1abe2dc81909c18aeae9b286898 completed March 9, 2026, 4:13 p.m.
PD Predicate disambiguation batch_69aee75b5b808190a348a31b1325d3d0 completed March 9, 2026, 3:29 p.m.
Created at: March 9, 2026, 3:21 p.m.