Triple

T11881193
Position Surface form Disambiguated ID Type / Status
Subject Words and Money E282660 entity
Predicate title P38 FINISHED
Object Words and Money E282660 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: Words and Money | Statement: [Words and Money, title, Words and Money]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Words and Money
Context triple: [Words and Money, title, Words and Money]
  • A. Words and Money chosen
    Words and Money is a critical examination of the commercialization of publishing and the media industry by editor and publisher André Schiffrin.
  • B. The Value of Money
    "The Value of Money" is an economic work by Prabhat Patnaik that critically examines the nature, role, and dynamics of money within capitalist economies.
  • C. Money
    Money is a personal finance magazine and media brand that provides advice and information on investing, saving, budgeting, and financial planning for consumers.
  • D. Money
    "Money" is a satirical comedy play by Edward Bulwer-Lytton that critiques the social power and moral influence of wealth in Victorian society.
  • E. Money
    "Money" is a well-known musical number from the stage production Cabaret that satirically explores greed and the corrupting influence of wealth.
  • 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_69d6ab2945d081908a5851c916cbcfb5 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8d39d2934819093b9f7006f45e5cb completed April 10, 2026, 10:40 a.m.
NED1 Entity disambiguation (via context triple) batch_69f417cb1c3881909ee50e8d11621664 completed May 1, 2026, 3:02 a.m.
Created at: April 8, 2026, 9:44 p.m.