Triple

T146101
Position Surface form Disambiguated ID Type / Status
Subject SyNAPSE neuromorphic computing program E3333 entity
Predicate fullName P16 FINISHED
Object Systems of Neuromorphic Adaptive Plastic Scalable Electronics E3333 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: Systems of Neuromorphic Adaptive Plastic Scalable Electronics | Statement: [SyNAPSE neuromorphic computing program, fullName, Systems of Neuromorphic Adaptive Plastic Scalable Electronics]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Systems of Neuromorphic Adaptive Plastic Scalable Electronics
Context triple: [SyNAPSE neuromorphic computing program, fullName, Systems of Neuromorphic Adaptive Plastic Scalable Electronics]
  • A. SyNAPSE neuromorphic computing program chosen
    The SyNAPSE neuromorphic computing program is a DARPA initiative to develop brain-inspired electronic systems that emulate neural architectures for highly efficient, scalable cognitive computing.
  • B. IEEE Transactions on Very Large Scale Integration (VLSI) Systems
    IEEE Transactions on Very Large Scale Integration (VLSI) Systems is a peer-reviewed scholarly journal focusing on the design, analysis, and implementation of VLSI and integrated systems.
  • 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. Neuralink
    Neuralink is a neurotechnology company developing implantable brain–computer interfaces aimed at enabling direct communication between the human brain and computers.
  • E. IEEE Circuits and Systems Magazine
    IEEE Circuits and Systems Magazine is a peer-reviewed periodical that features articles, tutorials, and reviews on advances in circuits, systems, and related signal processing technologies for researchers and professionals.
  • 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_69a252868de4819080e21c9938bfe8b6 completed Feb. 28, 2026, 2:27 a.m.
NER Named-entity recognition batch_69a257ea7eac8190884a53453a9e0dd6 completed Feb. 28, 2026, 2:50 a.m.
NED1 Entity disambiguation (via context triple) batch_69a2c2763ce481908c12046de9003a84 completed Feb. 28, 2026, 10:24 a.m.
Created at: Feb. 28, 2026, 2:31 a.m.