Triple

T5996413
Position Surface form Disambiguated ID Type / Status
Subject Steven Sinofsky E133480 entity
Predicate familyName P18 FINISHED
Object Sinofsky E133480 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: Sinofsky | Statement: [Steven Sinofsky, familyName, Sinofsky]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sinofsky
Context triple: [Steven Sinofsky, familyName, Sinofsky]
  • A. Steven Sinofsky chosen
    Steven Sinofsky is an American technology executive and former Microsoft president best known for leading the development of Windows and Office.
  • B. Eddy Cue
    Eddy Cue is a senior Apple executive best known for overseeing the company’s internet software and services, including iTunes, the App Store, and iCloud.
  • C. Lisa Su
    Lisa Su is a Taiwanese-American electrical engineer and business executive best known for leading AMD’s turnaround and growth as its chief executive.
  • D. Sundar Pichai
    Sundar Pichai is an Indian-American business executive and technologist best known for leading Google and its parent company Alphabet Inc. as chief executive officer.
  • E. Safra Catz
    Safra Catz is an Israeli-American business executive best known as the longtime CEO of Oracle Corporation and one of the most powerful figures in the global technology industry.
  • 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_69c00870ddbc81909880fa3864f4f38d completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c04e963f3c819082dd755e328ab947 completed March 22, 2026, 8:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69c10876d4d0819083ac7431c8abaedd completed March 23, 2026, 9:31 a.m.
Created at: March 22, 2026, 4:05 p.m.