Triple

T3564544
Position Surface form Disambiguated ID Type / Status
Subject Training Day E75412 entity
Predicate editedBy P1954 FINISHED
Object Conrad Buff IV E116007 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: Conrad Buff IV | Statement: [Training Day, editedBy, Conrad Buff IV]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Conrad Buff IV
Context triple: [Training Day, editedBy, Conrad Buff IV]
  • A. Conrad Buff IV chosen
    Conrad Buff IV is an American film editor known for his work on major Hollywood productions, including blockbusters like "Terminator Salvation."
  • B. Conrad Vig
    Conrad Vig is a dim-witted yet loyal and darkly comedic U.S. soldier portrayed by Spike Jonze in the 1999 Gulf War film "Three Kings."
  • C. Conrad Will
    Conrad Will was an early 19th-century American pioneer, politician, and businessman in Illinois, recognized as a prominent figure in the state's formative years.
  • D. Conrad Nagel
    Conrad Nagel was an American film and stage actor prominent during the silent and early sound eras, known for his sophisticated leading-man roles and long career in Hollywood.
  • E. Paul Conrad
    Paul Conrad was a prominent American political cartoonist renowned for his sharp, liberal-leaning commentary and long tenure at the Los Angeles Times.
  • 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_69ad85d512708190829c8b2d3a2ccfb8 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc0a76c008190a2056b6d990776fe completed March 8, 2026, 6:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69b3bba91d08819081a433c9f06a2c2b completed March 13, 2026, 7:24 a.m.
Created at: March 8, 2026, 3:21 p.m.