Triple

T8980630
Position Surface form Disambiguated ID Type / Status
Subject Daniel Neeson E214514 entity
Predicate relative P37 FINISHED
Object Corin Redgrave E516730 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: Corin Redgrave | Statement: [Daniel Neeson, relative, Corin Redgrave]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Corin Redgrave
Context triple: [Daniel Neeson, relative, Corin Redgrave]
  • A. Corin Redgrave chosen
    Corin Redgrave was an English stage and screen actor, and a member of the prominent Redgrave acting family, known for his work in classical theatre and numerous film and television roles.
  • B. Lynn Redgrave
    Lynn Redgrave was an acclaimed English actress from the famous Redgrave acting family, known for her versatile performances in film, television, and theatre.
  • C. Michael Redgrave
    Michael Redgrave was a distinguished English stage and film actor of the mid-20th century, acclaimed for his versatile performances in both classic theatre and notable British cinema.
  • D. Reggie Brown
    Reggie Brown is an American entrepreneur best known as a co-founder of Snapchat, the multimedia messaging app developed by Snap Inc.
  • E. Ron Todd
    Ron Todd is an American politician who served as the Kansas Insurance Commissioner before Kathleen Sebelius.
  • 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_69ca839ea8b88190922c6a326ffcc0d3 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc67a615b081909b88e761be879802 completed April 1, 2026, 12:32 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfd0b3ce0c81908a7b4297e0867cc9 completed April 3, 2026, 2:37 p.m.
Created at: March 30, 2026, 7:03 p.m.