Triple

T8669437
Position Surface form Disambiguated ID Type / Status
Subject Hayden Christensen E205757 entity
Predicate notableWork P4 FINISHED
Object Factory Girl E346634 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: Factory Girl | Statement: [Hayden Christensen, notableWork, Factory Girl]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Factory Girl
Context triple: [Hayden Christensen, notableWork, Factory Girl]
  • A. Factory Girl chosen
    Factory Girl is a 2006 biographical drama film about socialite and Warhol muse Edie Sedgwick, starring Sienna Miller in the lead role.
  • B. RSpec
    RSpec is a popular behavior-driven development (BDD) testing framework for the Ruby programming language, known for its readable, expressive syntax.
  • C. SuiteBuilder
    SuiteBuilder is a NetSuite configuration tool that lets users customize forms, fields, records, and user interface elements without needing to write code.
  • D. The Rack
    The Rack is a 1956 courtroom drama film about the psychological and moral aftermath of a Korean War veteran’s imprisonment and alleged collaboration with the enemy.
  • E. Ruby on Rails
    Ruby on Rails is a popular open-source web application framework that emphasizes convention over configuration and rapid development for building database-backed applications.
  • 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_69ca83516ae88190aefe034b3bc589e3 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc4917cb9881909a73b74e54250613 completed March 31, 2026, 10:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69cecd2b996481908da33fbd95494376 completed April 2, 2026, 8:10 p.m.
Created at: March 30, 2026, 6:31 p.m.