Triple

T21513061
Position Surface form Disambiguated ID Type / Status
Subject Heart E530774 entity
Predicate hasMember P10 FINISHED
Object Roger Fisher E1377660 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: Roger Fisher | Statement: [Heart, hasMember, Roger Fisher]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Roger Fisher
Context triple: [Heart, hasMember, Roger Fisher]
  • A. Kenneth Posner
    Kenneth Posner is a prominent American theatrical lighting designer known for his work on numerous Broadway productions.
  • B. James Hart
    James Hart is an American writer and memoirist best known for his marriage to singer-songwriter Carly Simon and his candid memoir about their life together.
  • C. Arthur Pearlstein chosen
    Arthur Pearlstein is an American lawyer and mediator known for his work in dispute resolution and public service, including leadership roles in federal mediation and conciliation efforts.
  • D. Karl Llewellyn
    Karl Llewellyn was a prominent 20th-century American legal scholar and leading figure of the legal realism movement, known especially for his role in drafting the Uniform Commercial Code.
  • E. Theodore V. Olsen
    Theodore V. Olsen was an American author best known for his Western novels, several of which, including the source material for the film "Soldier Blue," were adapted for the screen.
  • 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_69e0c45c81f08190a6b8bbb70a45aae7 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69e9ea8779c081908171c58d345d54ae completed April 23, 2026, 9:46 a.m.
NED1 Entity disambiguation (via context triple) batch_6a09e145ac5c8190a05cc2a80d28cac3 completed May 17, 2026, 3:39 p.m.
Created at: April 16, 2026, 6:25 p.m.