Triple

T15361063
Position Surface form Disambiguated ID Type / Status
Subject Olivier Bousquet E367288 entity
Predicate memberOf P10 FINISHED
Object Google Research E46728 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: Google Research | Statement: [Olivier Bousquet, memberOf, Google Research]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Google Research
Context triple: [Olivier Bousquet, memberOf, Google Research]
  • A. Google Research chosen
    Google Research is the research division of Google focused on advancing the state of the art in computer science and artificial intelligence through fundamental and applied research.
  • B. Adobe Research
    Adobe Research is the research arm of Adobe that advances cutting-edge technologies in areas such as computer graphics, computer vision, machine learning, and digital media.
  • C. Google X
    Google X is a semi-secret research and development lab of Google (now Alphabet) focused on creating breakthrough technologies such as autonomous vehicles and other ambitious "moonshot" projects.
  • D. Google Brain
    Google Brain is a deep learning research team at Google that pioneered many advances in neural networks and artificial intelligence.
  • E. Microsoft Research
    Microsoft Research is the research division of Microsoft, dedicated to advancing computer science and related fields through fundamental and applied research.
  • 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_69d85a1483788190ad93c2748e8af34b completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03e4607408190ab281a7f7a8012d3 completed April 16, 2026, 1:41 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff0b4a181c8190bffc1ac1a86e215d completed May 9, 2026, 10:24 a.m.
Created at: April 10, 2026, 3:18 a.m.