Triple

T6538665
Position Surface form Disambiguated ID Type / Status
Subject Laura (film) E168229 entity
Predicate screenwriter P2831 FINISHED
Object Samuel Hoffenstein E240474 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: Samuel Hoffenstein | Statement: [Laura (film), screenwriter, Samuel Hoffenstein]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Samuel Hoffenstein
Context triple: [Laura (film), screenwriter, Samuel Hoffenstein]
  • A. Samuel Hoffenstein chosen
    Samuel Hoffenstein was an American screenwriter and poet best known for his witty, sophisticated scripts in Hollywood films of the 1930s and 1940s.
  • B. Yehuda Hoffman
    Yehuda Hoffman is an astrophysicist known for his work in cosmology and large-scale structure, including helping identify and characterize the Laniakea Supercluster.
  • C. Louis Finkelstein
    Louis Finkelstein was a prominent American Conservative rabbi and scholar who served as a long-time leader and chancellor of the Jewish Theological Seminary of America.
  • D. Michael Waldstein
    Michael Waldstein is a Catholic theologian and scholar best known for his authoritative English translation and commentary on Pope John Paul II’s Theology of the Body.
  • E. Isaac Ehrlich
    Isaac Ehrlich is an economist known for his work on the economic analysis of crime, deterrence, and the death penalty.
  • 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_69c68a51564081909e93aee0dbd9cca3 completed March 27, 2026, 1:46 p.m.
NER Named-entity recognition batch_69c6add4b7f881909e485325c353f51c completed March 27, 2026, 4:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69c723af5fa88190acd0c040cdf24f13 completed March 28, 2026, 12:41 a.m.
Created at: March 27, 2026, 1:49 p.m.