Triple

T8962864
Position Surface form Disambiguated ID Type / Status
Subject Simon Kinberg E214050 entity
Predicate givenName P17 FINISHED
Object Simon E449149 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: Simon | Statement: [Simon Kinberg, givenName, Simon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Simon
Context triple: [Simon Kinberg, givenName, Simon]
  • A. Simon chosen
    Simon is a common masculine given name of Hebrew origin, widely used in many cultures and languages.
  • B. Simon
    Simon is the given name of Simon Bolivar Buckner Jr., a U.S. Army lieutenant general who was killed in action while commanding forces during the Battle of Okinawa in World War II.
  • C. Simon
    Simon is a common surname of English and Jewish origin borne by numerous notable individuals across politics, business, arts, and sciences.
  • D. Simon
    Simon is a sleazy used-car salesman and comic-relief character in the action-comedy film "True Lies," who pretends to be a secret agent to seduce women.
  • E. Simon
    Simon is the central character in Ang Lee's 1993 film "The Wedding Banquet," a Taiwanese American man who enters a sham marriage to appease his traditional parents while secretly living with his male partner in New York.
  • 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_69ca839cd6008190a1546a701a56710c completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc6749e5008190a01f42a2e772dd54 completed April 1, 2026, 12:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfc9512eec8190963aa68108691f7f completed April 3, 2026, 2:06 p.m.
Created at: March 30, 2026, 7:01 p.m.