Triple

T20221717
Position Surface form Disambiguated ID Type / Status
Subject SC-FDMA E495270 entity
Predicate usesTransform P17711 FINISHED
Object Discrete Fourier Transform 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: Discrete Fourier Transform | Statement: [SC-FDMA, usesTransform, Discrete Fourier Transform]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Discrete Fourier Transform
Context triple: [SC-FDMA, usesTransform, Discrete Fourier Transform]
  • A. Fourier transform chosen
    The Fourier transform is a mathematical operation that decomposes a function or signal into its constituent frequencies, widely used in engineering, physics, and signal processing.
  • B. FFT
    FFT is the French Tennis Federation, the national governing body for tennis in France and organizer of major tournaments including the French Open.
  • C. FFT
    FFT is the IATA airport code for Capital City Airport in Kentucky, United States.
  • D. FFT
    FFT is the ICAO airline designator used in aviation to identify Frontier Airlines in flight plans and air traffic control communications.
  • E. Walsh–Hadamard transform
    The Walsh–Hadamard transform is an orthogonal, non-sinusoidal signal transform that decomposes data into a basis of square-wave-like functions, widely used in communications, coding theory, and signal processing.
  • 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_69da626cff80819097b530718a7c98b6 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e66fd610f881908fdd22b1f8bd2efc completed April 20, 2026, 6:26 p.m.
Created at: April 11, 2026, 11:39 p.m.