Triple

T16614199
Position Surface form Disambiguated ID Type / Status
Subject Alexei Kitaev E403650 entity
Predicate notableWork P4 FINISHED
Object Kitaev quantum phase estimation algorithm
The Kitaev quantum phase estimation algorithm is a foundational quantum computing procedure introduced by Alexei Kitaev that efficiently extracts eigenphase information of unitary operators, underpinning many quantum algorithms such as Shor’s factoring algorithm.
E1223602 NE FINISHED

How this triple was built (4 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: Kitaev quantum phase estimation algorithm | Statement: [Alexei Kitaev, notableWork, Kitaev quantum phase estimation algorithm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kitaev quantum phase estimation algorithm
Context triple: [Alexei Kitaev, notableWork, Kitaev quantum phase estimation algorithm]
  • A. Grover’s algorithm
    Grover’s algorithm is a quantum search algorithm that provides a quadratic speedup over classical methods for finding a marked item in an unsorted database.
  • B. The Learnability of Quantum States
    "The Learnability of Quantum States" is a research paper by Scott Aaronson that investigates under what conditions quantum states can be efficiently learned or approximated from measurement data within the framework of computational learning theory.
  • C. Bernstein–Vazirani algorithm
    The Bernstein–Vazirani algorithm is a quantum algorithm that efficiently determines a hidden binary string using a single query to an oracle, illustrating quantum speedup over classical methods.
  • D. Deutsch–Jozsa algorithm
    The Deutsch–Jozsa algorithm is a foundational quantum algorithm that demonstrates how quantum computation can solve certain decision problems exponentially faster than any classical deterministic algorithm.
  • E. The Complexity of Quantum States and Transformations
    "The Complexity of Quantum States and Transformations" is a highly cited research paper by Scott Aaronson that investigates the computational complexity and limitations of describing, preparing, and manipulating quantum states and operations.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Kitaev quantum phase estimation algorithm
Triple: [Alexei Kitaev, notableWork, Kitaev quantum phase estimation algorithm]
Generated description
The Kitaev quantum phase estimation algorithm is a foundational quantum computing procedure introduced by Alexei Kitaev that efficiently extracts eigenphase information of unitary operators, underpinning many quantum algorithms such as Shor’s factoring algorithm.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Kitaev quantum phase estimation algorithm
Target entity description: The Kitaev quantum phase estimation algorithm is a foundational quantum computing procedure introduced by Alexei Kitaev that efficiently extracts eigenphase information of unitary operators, underpinning many quantum algorithms such as Shor’s factoring algorithm.
  • A. Grover’s algorithm
    Grover’s algorithm is a quantum search algorithm that provides a quadratic speedup over classical methods for finding a marked item in an unsorted database.
  • B. The Learnability of Quantum States
    "The Learnability of Quantum States" is a research paper by Scott Aaronson that investigates under what conditions quantum states can be efficiently learned or approximated from measurement data within the framework of computational learning theory.
  • C. Bernstein–Vazirani algorithm
    The Bernstein–Vazirani algorithm is a quantum algorithm that efficiently determines a hidden binary string using a single query to an oracle, illustrating quantum speedup over classical methods.
  • D. Deutsch–Jozsa algorithm
    The Deutsch–Jozsa algorithm is a foundational quantum algorithm that demonstrates how quantum computation can solve certain decision problems exponentially faster than any classical deterministic algorithm.
  • E. The Complexity of Quantum States and Transformations
    "The Complexity of Quantum States and Transformations" is a highly cited research paper by Scott Aaronson that investigates the computational complexity and limitations of describing, preparing, and manipulating quantum states and operations.
  • F. None of above. chosen

Provenance (5 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_69d883897eb481909eaaa088ba9918d9 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e360983d2c8190b1fe7f18aedfbde1 completed April 18, 2026, 10:44 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0075aeaa9881908bdef0f9f2b52e60 completed May 10, 2026, 12:10 p.m.
NEDg Description generation batch_6a007705f57881908b07a20ae8957c64 completed May 10, 2026, 12:16 p.m.
NED2 Entity disambiguation (via description) batch_6a007b18f0b08190a9ddc6ad7358d6b8 completed May 10, 2026, 12:33 p.m.
Created at: April 10, 2026, 5:17 a.m.