Triple

T10296049
Position Surface form Disambiguated ID Type / Status
Subject David Duffield E241490 entity
Predicate notableWork P4 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: [David Duffield, notableWork, PeopleSoft]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: PeopleSoft
Context triple: [David Duffield, notableWork, 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. Allaire Corporation
    Allaire Corporation was a software company best known for creating the ColdFusion web application development platform before being acquired by Macromedia.
  • 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_69d381aaafc08190af475ef58dc16aba completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4d2ea9b3c8190b11518b259d5825c completed April 7, 2026, 9:48 a.m.
NED1 Entity disambiguation (via context triple) batch_69d71d23f49081909aea149c6b219354 completed April 9, 2026, 3:29 a.m.
Created at: April 6, 2026, 11:43 a.m.