Triple

T2993536
Position Surface form Disambiguated ID Type / Status
Subject Simon Osindero E81011 entity
Predicate coAuthorWith P398 FINISHED
Object Yee-Whye Teh E80657 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: Yee-Whye Teh | Statement: [Simon Osindero, coAuthorWith, Yee-Whye Teh]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Yee-Whye Teh
Context triple: [Simon Osindero, coAuthorWith, Yee-Whye Teh]
  • A. Yee-Whye Teh chosen
    Yee-Whye Teh is a prominent statistician and machine learning researcher known for his influential work on Bayesian nonparametrics, probabilistic modeling, and deep learning.
  • B. Mung Chiang
    Mung Chiang is an engineer and academic leader known for his work in electrical and computer engineering and for serving as president of Purdue University.
  • C. Tung-Mow Yan
    Tung-Mow Yan is a theoretical physicist best known for co-formulating the Drell–Yan process, a fundamental mechanism for lepton pair production in high-energy particle collisions.
  • D. Yu-Chi Ho
    Yu-Chi Ho is a prominent control theorist and engineer known for his pioneering contributions to optimal control, dynamic systems, and game theory.
  • E. Yeou-Cheng Ma
    Yeou-Cheng Ma is a Taiwanese-American pediatrician and violinist known for her work in child development and her contributions to music education.
  • 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_69ad8b187fc8819085914d3c9ea3142d completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad99e291e0819089f81c0a7d7a6cd9 completed March 8, 2026, 3:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69b109061684819086777d3b871c94f8 completed March 11, 2026, 6:17 a.m.
Created at: March 8, 2026, 2:59 p.m.