Triple

T10635094
Position Surface form Disambiguated ID Type / Status
Subject Miriam Bienstock E250558 entity
Predicate name P16 FINISHED
Object Miriam Bienstock E250558 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: Miriam Bienstock | Statement: [Miriam Bienstock, name, Miriam Bienstock]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Miriam Bienstock
Context triple: [Miriam Bienstock, name, Miriam Bienstock]
  • A. Miriam Bienstock chosen
    Miriam Bienstock was an American music industry executive and co-founder of Atlantic Records who played a key role in shaping the label’s early business operations and success.
  • B. Miriam Weinstein
    Miriam Weinstein is the mother of film producer Harvey Weinstein, whose first name inspired the name of the film company Miramax.
  • C. Miriam Fried
    Miriam Fried is an acclaimed Israeli-American violinist renowned for her solo performances, chamber music collaborations, and influential teaching career.
  • D. Miriam Mendelsohn
    Miriam Mendelsohn is a loyal, upbeat, and supportive best friend of Mei Lee in Pixar's animated film "Turning Red."
  • E. Elisheva Rotstein
    Elisheva Rotstein is a fictional character portrayed by Israeli actress Ayelet Zurer, likely featured in an Israeli film or television production.
  • 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_69d6aa5993448190a493b790b8f85010 completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d6dfac70f481908363f9ac0b651fbe completed April 8, 2026, 11:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69e2161018408190bcb64efba0974f8c completed April 17, 2026, 11:14 a.m.
Created at: April 8, 2026, 9:03 p.m.