Triple

T1565276
Position Surface form Disambiguated ID Type / Status
Subject Azim Premji E33418 entity
Predicate employer P7 FINISHED
Object Wipro Limited E31804 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: Wipro Limited | Statement: [Azim Premji, employer, Wipro Limited]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wipro Limited
Context triple: [Azim Premji, employer, Wipro Limited]
  • A. Wipro Limited chosen
    Wipro Limited is a major Indian multinational information technology, consulting, and business process services company headquartered in Bengaluru.
  • B. Infosys
    Infosys is a leading Indian multinational IT services and consulting company known for its global technology solutions and innovation initiatives.
  • C. Capgemini
    Capgemini is a global consulting, technology services, and digital transformation company headquartered in France.
  • D. Reliance Industries Limited
    Reliance Industries Limited is a major Indian multinational conglomerate with leading businesses in energy, petrochemicals, retail, and telecommunications.
  • E. Wipro GE Healthcare
    Wipro GE Healthcare is a joint venture between Wipro Limited and GE Healthcare that develops and provides medical imaging, diagnostics, and healthcare technology solutions, primarily serving the Indian and emerging markets.
  • 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_69a885f11b048190935025a035302715 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69aa621242188190a7e1deeada7688d8 completed March 6, 2026, 5:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad3719dfdc819085511f96f9f92dba completed March 8, 2026, 8:45 a.m.
Created at: March 4, 2026, 7:27 p.m.