Triple

T4268999
Position Surface form Disambiguated ID Type / Status
Subject Daughter of Shanghai E96893 entity
Predicate director P255 FINISHED
Object Robert Florey E215311 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: Robert Florey | Statement: [Daughter of Shanghai, director, Robert Florey]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Robert Florey
Context triple: [Daughter of Shanghai, director, Robert Florey]
  • A. Robert Florey chosen
    Robert Florey was a French-American film director and screenwriter known for his work in early Hollywood horror and experimental cinema.
  • B. Frank Losee
    Frank Losee was an American stage and silent film actor active in the early 20th century.
  • C. Val Fitch
    Val Fitch was an American nuclear physicist and Nobel laureate renowned for his co-discovery of CP violation in the decay of neutral K-mesons, a finding that profoundly influenced particle physics and cosmology.
  • D. Donald F. Hornig
    Donald F. Hornig was an American chemist and science advisor who served as a key presidential science counselor, notably to President Lyndon B. Johnson.
  • E. Melvin Wydler
    Melvin Wydler was a U.S. Congressman whose legislative work on technology and innovation policy led to a federal law being named in his honor.
  • 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_69b34543f06c8190915ebb1a4574ffa9 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b34ff913608190b6ccf4a85057b07b completed March 12, 2026, 11:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5b79fe8c08190b4a9e4812babc78e completed March 14, 2026, 7:31 p.m.
Created at: March 12, 2026, 11:07 p.m.