Triple

T15312168
Position Surface form Disambiguated ID Type / Status
Subject A Simple Favor E366063 entity
Predicate producer P490 FINISHED
Object Laura Fischer
Laura Fischer is a film producer known for her work on the darkly comedic thriller "A Simple Favor."
E1154520 NE FINISHED

How this triple was built (4 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: Laura Fischer | Statement: [A Simple Favor, producer, Laura Fischer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Laura Fischer
Context triple: [A Simple Favor, producer, Laura Fischer]
  • A. Ann Fischer
    Ann Fischer was the wife of prominent American television newscaster David Brinkley.
  • B. Johanna Fiedler
    Johanna Fiedler was an American arts publicist and author, best known for her work with the Metropolitan Opera and her books on classical music figures and institutions.
  • C. Kristel Fuchs
    Kristel Fuchs is a sibling of Klaus Fuchs, the German-born theoretical physicist who was convicted of spying for the Soviet Union during the Cold War.
  • D. Eveline Fischer
    Eveline Fischer is a video game composer best known for her work on classic Rare titles, including contributions to the Donkey Kong Country series.
  • E. Emilie Hainisch
    Emilie Hainisch was the wife of Austrian politician and first Federal President Michael Hainisch and a member of a prominent Austrian family in the early 20th century.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Laura Fischer
Triple: [A Simple Favor, producer, Laura Fischer]
Generated description
Laura Fischer is a film producer known for her work on the darkly comedic thriller "A Simple Favor."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Laura Fischer
Target entity description: Laura Fischer is a film producer known for her work on the darkly comedic thriller "A Simple Favor."
  • A. Ann Fischer
    Ann Fischer was the wife of prominent American television newscaster David Brinkley.
  • B. Johanna Fiedler
    Johanna Fiedler was an American arts publicist and author, best known for her work with the Metropolitan Opera and her books on classical music figures and institutions.
  • C. Kristel Fuchs
    Kristel Fuchs is a sibling of Klaus Fuchs, the German-born theoretical physicist who was convicted of spying for the Soviet Union during the Cold War.
  • D. Eveline Fischer
    Eveline Fischer is a video game composer best known for her work on classic Rare titles, including contributions to the Donkey Kong Country series.
  • E. Emilie Hainisch
    Emilie Hainisch was the wife of Austrian politician and first Federal President Michael Hainisch and a member of a prominent Austrian family in the early 20th century.
  • F. None of above. chosen

Provenance (5 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_69d85a113ee881908e297a1d38dd79fa completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03cd2d5a88190aead748920f93d47 completed April 16, 2026, 1:35 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff133d171c8190918c9624bcdb7451 completed May 9, 2026, 10:58 a.m.
NEDg Description generation batch_69ff142e99e081909d01cac0416f1bde completed May 9, 2026, 11:02 a.m.
NED2 Entity disambiguation (via description) batch_69ff14c61eb08190ba854b541eb1ce14 completed May 9, 2026, 11:04 a.m.
Created at: April 10, 2026, 3:16 a.m.