Triple

T17841235
Position Surface form Disambiguated ID Type / Status
Subject Jong Wook Kim E445531 entity
Predicate contributedTo P37 FINISHED
Object CLIP: Connecting Text and Images 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: CLIP: Connecting Text and Images | Statement: [Jong Wook Kim, contributedTo, CLIP: Connecting Text and Images]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: CLIP: Connecting Text and Images
Context triple: [Jong Wook Kim, contributedTo, CLIP: Connecting Text and Images]
  • A. CLIP chosen
    CLIP is an OpenAI model that learns joint representations of images and text, enabling tasks like zero-shot image classification and natural language-based image retrieval.
  • B. Show and Tell: A Neural Image Caption Generator
    "Show and Tell: A Neural Image Caption Generator" is a pioneering deep learning model that automatically generates natural-language descriptions for images by combining convolutional and recurrent neural networks.
  • C. 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.
  • D. 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.
  • E. Images and Words
    Images and Words is a landmark 1992 progressive metal album by Dream Theater, widely credited with bringing the band mainstream recognition and defining their signature sound.
  • 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_69d8b9f1a6d881909f024bc603111cdb completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e48d2b2ea08190926ec0cf01285833 completed April 19, 2026, 8:07 a.m.
Created at: April 10, 2026, 10:16 a.m.