Triple

T14807162
Position Surface form Disambiguated ID Type / Status
Subject Little Odessa E348066 entity
Predicate cinematographyBy P1953 FINISHED
Object Tom Richmond E833370 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: Tom Richmond | Statement: [Little Odessa, cinematographyBy, Tom Richmond]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tom Richmond
Context triple: [Little Odessa, cinematographyBy, Tom Richmond]
  • A. Tom Richmond chosen
    Tom Richmond is an American cinematographer known for his work on independent and genre films, including Rob Zombie’s horror movie "House of 1000 Corpses."
  • B. Archie Rice
    Archie Rice is a washed-up, cynical music-hall performer whose personal and professional decline embodies the fading glory of British vaudeville.
  • C. Joe Guinn
    Joe Guinn was a civil rights activist best known for helping establish the Congress of Racial Equality, a key organization in the U.S. civil rights movement.
  • D. Charlie Ward
    Charlie Ward is a former Florida State University quarterback who won the 1993 Heisman Trophy and later played professional basketball in the NBA.
  • E. Charlie Sitton
    Charlie Sitton is a former American college basketball standout best known for his All-American career at Oregon State University in the early 1980s.
  • 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_69d822ea8b7c819097dfadf3d45545e6 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69decf33b6a08190ab6a4cfeda2cc09c completed April 14, 2026, 11:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe9dbced588190ab7712c7ad50ee67 completed May 9, 2026, 2:36 a.m.
Created at: April 10, 2026, 1:41 a.m.