Triple

T15403062
Position Surface form Disambiguated ID Type / Status
Subject Serguei Mourachov E368373 entity
Predicate employer P7 FINISHED
Object Microsoft E1649 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: Microsoft | Statement: [Serguei Mourachov, employer, Microsoft]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Microsoft
Context triple: [Serguei Mourachov, employer, Microsoft]
  • A. Microsoft chosen
    Microsoft is a multinational technology company best known for its Windows operating system, Office productivity suite, and Azure cloud computing platform.
  • B. Micros Systems
    Micros Systems was a leading provider of point-of-sale and hospitality management software and hardware solutions for restaurants, hotels, and retail businesses.
  • C. WIN Corporation
    WIN Corporation is an Australian media company best known for owning and operating the WIN Television network and related broadcasting assets.
  • D. Microsoft Imagine
    Microsoft Imagine was an academic program by Microsoft that provided students and educators with free or discounted access to professional developer and design tools, later rebranded as Azure Dev Tools for Teaching.
  • E. Microsoft Auto
    Microsoft Auto is an embedded automotive software platform developed by Microsoft to power in-car infotainment and navigation systems.
  • 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_69d85a16c68c819099c1b547fbc87b32 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03e8ea0ac8190a5c68b1951ad3db1 completed April 16, 2026, 1:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff13584f8881908b2527c51f85ae28 completed May 9, 2026, 10:58 a.m.
Created at: April 10, 2026, 3:19 a.m.