Triple

T14875309
Position Surface form Disambiguated ID Type / Status
Subject Financial District, Hyderabad E349851 entity
Predicate hasCompany P1287 FINISHED
Object Cognizant E695869 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: Cognizant | Statement: [Financial District, Hyderabad, hasCompany, Cognizant]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cognizant
Context triple: [Financial District, Hyderabad, hasCompany, Cognizant]
  • A. Cognizant chosen
    Cognizant is a multinational information technology services and consulting company known for providing digital, technology, consulting, and operations services to clients worldwide.
  • B. Capgemini
    Capgemini is a global consulting, technology services, and digital transformation company headquartered in France.
  • C. Infosys
    Infosys is a leading Indian multinational IT services and consulting company known for its global technology solutions and innovation initiatives.
  • D. 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.
  • E. Accenture
    Accenture is a global professional services company specializing in consulting, technology, and outsourcing solutions for businesses and governments worldwide.
  • 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_69d822ee4f408190b6ac3b2fa434f0df completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69ded5e3e5d48190a132f2cf012b01e2 completed April 15, 2026, 12:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe7e7fc904819094269b7c785ead69 completed May 9, 2026, 12:23 a.m.
Created at: April 10, 2026, 1:55 a.m.