Triple

T10397036
Position Surface form Disambiguated ID Type / Status
Subject Circus E245046 entity
Predicate hasDivision P35 FINISHED
Object Mothers E186737 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: Mothers | Statement: [Circus, hasDivision, Mothers]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mothers
Context triple: [Circus, hasDivision, Mothers]
  • A. Mothers chosen
    Mothers is a section of the Tokyo Stock Exchange dedicated to emerging and high-growth startup companies seeking public investment.
  • B. Mothers Are Women
    Mothers Are Women is a Canadian documentary film by Bonnie Sherr Klein that explores the lives and challenges of disabled mothers.
  • C. Four Mothers
    Four Mothers is a 1941 American drama film that continues the story of the Lemp family introduced in Four Daughters and its first sequel, Four Wives.
  • D. The Many Mothers
    The Many Mothers is a matriarchal group of women in the Mad Max universe who inhabit and lead the Green Place, a once-fertile oasis.
  • E. Mother and Child
    "Mother and Child" is a 2009 drama film written and directed by Rodrigo García that interweaves the emotional stories of three women connected by adoption and motherhood.
  • 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_69d381b5116081908d85227bab6d3c0c completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4e9d0de448190b0bfd4d6c87d47fa completed April 7, 2026, 11:26 a.m.
NED1 Entity disambiguation (via context triple) batch_69d795cf331c8190b35caf3997dc29a3 completed April 9, 2026, 12:04 p.m.
Created at: April 6, 2026, 12:06 p.m.