Triple

T13653118
Position Surface form Disambiguated ID Type / Status
Subject Scene Completion Using Millions of Photographs E326788 entity
Predicate title P38 FINISHED
Object Scene Completion Using Millions of Photographs E326788 NE 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: Scene Completion Using Millions of Photographs | Statement: [Scene Completion Using Millions of Photographs, title, Scene Completion Using Millions of Photographs]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Scene Completion Using Millions of Photographs
Context triple: [Scene Completion Using Millions of Photographs, title, Scene Completion Using Millions of Photographs]
  • A. Scene Completion Using Millions of Photographs chosen
    "Scene Completion Using Millions of Photographs" is a seminal computer vision and graphics paper that introduced a data-driven method for automatically filling in missing regions of images by searching a massive online photo collection for visually compatible patches.
  • B. Still Image Architecture
    Still Image Architecture was an early Windows technology framework for communicating with scanners and digital cameras that was later superseded by Windows Image Acquisition.
  • C. Learning to See
    "Learning to See" is an autobiographical essay by Eudora Welty that reflects on how her early experiences and observations shaped her development as a writer.
  • D. Long-term Recurrent Convolutional Networks for Visual Recognition and Description
    "Long-term Recurrent Convolutional Networks for Visual Recognition and Description" is a research paper that introduces a deep learning architecture combining convolutional and recurrent neural networks to perform tasks like video recognition and automatic image or video captioning.
  • E. ImageNet Classification with Deep Convolutional Neural Networks
    "ImageNet Classification with Deep Convolutional Neural Networks" is the landmark 2012 research paper that introduced the deep CNN model AlexNet, demonstrating a dramatic leap in image recognition performance on the ImageNet benchmark and catalyzing the modern deep learning revolution in computer vision.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69d8076d8270819092afc2f0e9c359a8 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbc609676c8190b5b1cabe6b315142 completed April 12, 2026, 4:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69f78affa3c481909dba71e2ce9f44c1 completed May 3, 2026, 5:50 p.m.
Created at: April 9, 2026, 9:52 p.m.