Triple

T804757
Position Surface form Disambiguated ID Type / Status
Subject L2M E17403 entity
Predicate relatedTo P37 FINISHED
Object DARPA Lifelong Learning Machines program E3332 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: DARPA Lifelong Learning Machines program | Statement: [L2M, relatedTo, DARPA Lifelong Learning Machines program]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: DARPA Lifelong Learning Machines program
Context triple: [L2M, relatedTo, DARPA Lifelong Learning Machines program]
  • A. Lifelong Learning Machines program chosen
    The Lifelong Learning Machines program is a DARPA research initiative aimed at developing AI systems that can continuously learn and adapt from experience in dynamic, real-world environments.
  • B. "A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence"
    "A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence" is the seminal 1955 research proposal by John McCarthy and colleagues that launched the field of artificial intelligence by defining its goals and organizing the landmark 1956 Dartmouth conference.
  • C. “Learning representations by back-propagating errors”
    “Learning representations by back-propagating errors” is a landmark 1986 research paper that popularized the backpropagation algorithm for training multi-layer neural networks, helping to launch the modern field of deep learning.
  • D. Technical Committee on Robot Learning
    The Technical Committee on Robot Learning is a specialized IEEE Robotics and Automation Society group that advances research and collaboration at the intersection of machine learning and robotics.
  • E. Gradient-based learning applied to document recognition
    "Gradient-based learning applied to document recognition" is a seminal 1998 paper by Yann LeCun and colleagues that introduced and demonstrated the effectiveness of convolutional neural networks for tasks like handwritten digit recognition, helping to lay the foundations of modern deep learning.
  • 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_69a4937ae8a08190b5084a03d532b30e completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4aabff3d88190bec4299fa0d87df0 completed March 1, 2026, 9:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7928af9d0819095b28e738a0f60dd completed March 4, 2026, 2:01 a.m.
Created at: March 1, 2026, 7:38 p.m.