Triple

T17904112
Position Surface form Disambiguated ID Type / Status
Subject Barbie Ferreira E447655 entity
Predicate representedBy P1748 FINISHED
Object IMG Models 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: IMG Models | Statement: [Barbie Ferreira, representedBy, IMG Models]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: IMG Models
Context triple: [Barbie Ferreira, representedBy, IMG Models]
  • A. IMG Models (historically) chosen
    IMG Models is a leading international modeling agency known for representing many of the world’s most prominent fashion models and talent.
  • B. IMG
    IMG is a global sports, events, and talent management company known for representing athletes and models and producing major sporting and fashion events.
  • C. Imagen Foundation
    Imagen Foundation is a nonprofit organization dedicated to promoting positive and accurate portrayals of Latinos in the entertainment industry, best known for organizing the annual Imagen Awards.
  • D. VisionEncoderDecoderModel
    VisionEncoderDecoderModel is a Hugging Face Transformers architecture that combines a vision encoder with a text decoder to perform tasks like image captioning and visual question answering.
  • E. DALL·E
    DALL·E is an AI model developed by OpenAI that generates images from natural language descriptions, enabling text-to-image synthesis.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8b9f6d394819082a6d69fd1e23d2f completed April 10, 2026, 8:51 a.m.
NER Named-entity recognition batch_69e49e9a9cfc8190879fc36dfdeb562b completed April 19, 2026, 9:21 a.m.
Created at: April 10, 2026, 10:19 a.m.