Triple

T20803383
Position Surface form Disambiguated ID Type / Status
Subject Accenture E512094 entity
Predicate hasBusinessUnit P7588 FINISHED
Object Accenture Operations NE NERFINISHED

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: Accenture Operations | Statement: [Accenture, hasBusinessUnit, Accenture Operations]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Accenture Operations
Context triple: [Accenture, hasBusinessUnit, Accenture Operations]
  • A. Accenture chosen
    Accenture is a global professional services company specializing in consulting, technology, and outsourcing solutions for businesses and governments worldwide.
  • B. Cognizant
    Cognizant is a multinational information technology services and consulting company known for providing digital, technology, consulting, and operations services to clients worldwide.
  • C. DXC Technology
    DXC Technology is a global IT services and consulting company that provides technology solutions and outsourcing services to enterprises and governments worldwide.
  • D. Capgemini
    Capgemini is a global consulting, technology services, and digital transformation company headquartered in France.
  • E. HCL Technologies
    HCL Technologies is a global Indian IT services and consulting company known for providing software development, infrastructure management, and digital transformation solutions to enterprises worldwide.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4cc69f481908e98751e697b9df4 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c2b2d5688190aaa58a2594d4787c completed April 21, 2026, 12:20 a.m.
Created at: April 16, 2026, 12:39 p.m.