Triple

T13797328
Position Surface form Disambiguated ID Type / Status
Subject Tears of the Sun E331550 entity
Predicate producer P490 FINISHED
Object Mike Lobell E859499 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: Mike Lobell | Statement: [Tears of the Sun, producer, Mike Lobell]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mike Lobell
Context triple: [Tears of the Sun, producer, Mike Lobell]
  • A. Mike Lobell chosen
    Mike Lobell is an American film producer known for his work on a range of Hollywood movies, including political thrillers and comedies.
  • B. Jeffrey Rodman
    Jeffrey Rodman is an American engineer and entrepreneur best known as the co-founder of Polycom, a company specializing in audio and video conferencing technologies.
  • C. John Boettiger
    John Boettiger was an American journalist and newspaper publisher best known as the second husband of Anna Roosevelt, daughter of President Franklin D. Roosevelt.
  • D. Tim McIlrath
    Tim McIlrath is an American musician best known as the lead vocalist, rhythm guitarist, and primary songwriter of the punk rock band Rise Against.
  • E. Kyle B. O’Boyle
    Kyle B. O’Boyle is a comedic UPS delivery man character in the Broadway musical "Legally Blonde," known for his flirtatious charm and memorable entrance.
  • 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_69d81c58feb08190a77bca8bf7d6d20f completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de025be1f08190aac525d72d7dc0c3 completed April 14, 2026, 9:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7c0e5251c81909f3f40dcdea1772f completed May 3, 2026, 9:40 p.m.
Created at: April 9, 2026, 10:11 p.m.