Triple

T5452102
Position Surface form Disambiguated ID Type / Status
Subject PeopleCode E122392 entity
Predicate developer P73 FINISHED
Object PeopleSoft E21777 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: PeopleSoft | Statement: [PeopleCode, developer, PeopleSoft]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: PeopleSoft
Context triple: [PeopleCode, developer, PeopleSoft]
  • A. PeopleSoft chosen
    PeopleSoft is an enterprise software company best known for its human resources and financial management applications, later integrated into Oracle’s product portfolio.
  • B. Siebel Systems
    Siebel Systems was a leading enterprise software company best known for pioneering customer relationship management (CRM) solutions for large organizations.
  • C. BEA Systems
    BEA Systems was a software company best known for its enterprise middleware and application server products that played a major role in early Java-based web and enterprise computing.
  • D. Siebel
    Siebel is a surname most prominently associated with Jennifer Siebel Newsom, an American documentary filmmaker and the First Partner of California.
  • E. Perot Systems
    Perot Systems was an American information technology services and consulting company founded by Ross Perot that provided outsourcing, systems integration, and technology solutions to 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_69bd46424248819085282ddf50a565f3 completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd91dfec248190af6f9c793a99c34c completed March 20, 2026, 6:28 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf4883bfec8190bb09fff99d017111 completed March 22, 2026, 1:40 a.m.
Created at: March 20, 2026, 2:08 p.m.