Triple

T12267276
Position Surface form Disambiguated ID Type / Status
Subject Conditional GAN E292378 entity
Predicate architectureCanUse P104134 FINISHED
Object convolutional neural networks 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: convolutional neural networks | Statement: [Conditional GAN, architectureCanUse, convolutional neural networks]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: architectureCanUse
Context triple: [Conditional GAN, architectureCanUse, convolutional neural networks]
  • A. architecturalUse
    Indicates how a structure, space, or element is intended to be used or function within an architectural context.
  • B. architecturalPlanner
    Indicates a relationship where an entity is responsible for designing, organizing, or planning the architectural structure or layout of another entity.
  • C. architectureType
    Indicates the specific style or category of architecture that characterizes or defines an entity.
  • D. architecturalOrderUsed
    Indicates that a particular architectural order (e.g., Doric, Ionic, Corinthian) is employed in the design or construction of a structure or its elements.
  • E. architectureName
    Indicates the specific name or title assigned to an architecture.
  • 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_69d6ab6856488190b5d31178d5015f8e completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d9380a5e78819086bd4dfe9a83d1f5 completed April 10, 2026, 5:48 p.m.
PD Predicate disambiguation batch_69d91c4a66cc819083ce6fcaf5042af6 completed April 10, 2026, 3:50 p.m.
PDg Predicate description generation batch_69d93805cee08190a532ebcf5908e617 completed April 10, 2026, 5:48 p.m.
Created at: April 8, 2026, 9:52 p.m.