Triple

T15670730
Position Surface form Disambiguated ID Type / Status
Subject Greg LeMond E377303 entity
Predicate team P3756 FINISHED
Object GAN E983688 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: GAN | Statement: [Greg LeMond, team, GAN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: GAN
Context triple: [Greg LeMond, team, GAN]
  • A. GAN
    GAN (Generative Adversarial Network) is a machine learning framework in which two neural networks compete in a zero-sum game to generate realistic synthetic data such as images, audio, or text.
  • B. GAN chosen
    GAN is a major French insurance company that was partially privatized during the Jospin government’s economic reforms.
  • C. Conditional GAN
    A Conditional GAN is a type of generative adversarial network that produces data samples conditioned on auxiliary information such as class labels or input images, enabling controlled and targeted generation.
  • D. StyleGAN
    StyleGAN is a state-of-the-art generative adversarial network architecture known for producing highly realistic, controllable images by manipulating disentangled style representations at different layers of the network.
  • E. Progressive GAN
    Progressive GAN is a generative adversarial network architecture that grows both the generator and discriminator layers progressively during training to produce high-resolution, high-quality synthetic images.
  • 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_69d85cd2e28481909d4e975bee20872f completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04f1254508190a77a16b7bfd299ad completed April 16, 2026, 2:53 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff67a431208190b7e0d1eefd55504a completed May 9, 2026, 4:58 p.m.
Created at: April 10, 2026, 4:16 a.m.