Triple

T7354057
Position Surface form Disambiguated ID Type / Status
Subject The Girl Who Had Everything E169577 entity
Predicate cinematographyBy P1953 FINISHED
Object Paul Vogel E25375 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: Paul Vogel | Statement: [The Girl Who Had Everything, cinematographyBy, Paul Vogel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Paul Vogel
Context triple: [The Girl Who Had Everything, cinematographyBy, Paul Vogel]
  • A. Paul Vogel chosen
    Paul Vogel was an American cinematographer best known for his work on classic Hollywood films, including the Oscar-winning "Battleground."
  • B. Gene Shue
    Gene Shue was an American professional basketball player and longtime NBA head coach known for revitalizing struggling franchises and leading multiple teams deep into the playoffs.
  • C. Bruce Weitz
    Bruce Weitz is an American actor best known for his Emmy-winning role as the eccentric detective Mick Belker on the television series "Hill Street Blues."
  • D. David Dorfman
    David Dorfman is an American actor best known for playing the young boy Aidan Keller in the horror film "The Ring" and its sequel.
  • E. Philip Dunne
    Philip Dunne was an American screenwriter, director, and producer best known for his work on classic Hollywood films from the 1930s through the 1960s.
  • 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_69c68a59f2288190877ca15c19b1e822 completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f10e71fc81909307ca39a61142d3 completed March 27, 2026, 9:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69c810d0aebc8190a7274fbcd3fe11ff completed March 28, 2026, 5:33 p.m.
Created at: March 27, 2026, 3:05 p.m.