Triple

T10089891
Position Surface form Disambiguated ID Type / Status
Subject Victor Francen E215314 entity
Predicate name P16 FINISHED
Object Victor Francen E215314 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: Victor Francen | Statement: [Victor Francen, name, Victor Francen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Victor Francen
Context triple: [Victor Francen, name, Victor Francen]
  • A. Victor Francen chosen
    Victor Francen was a Belgian-born French actor known for his distinguished presence in European and Hollywood films of the 1930s and 1940s.
  • B. Victor Van Dort
    Victor Van Dort is the shy, nervous protagonist of Tim Burton’s animated film "Corpse Bride," whose accidental marriage to a deceased bride entangles him in a gothic romance between the worlds of the living and the dead.
  • C. Marius de Vries
    Marius de Vries is a British composer, producer, and arranger known for his innovative work on film soundtracks and collaborations with prominent pop and electronic artists.
  • D. Jean van Duren
    Jean van Duren was an 18th-century publisher known for issuing the political treatise "Anti-Machiavel," often associated with Frederick the Great.
  • E. Anton de Berghmann
    Anton de Berghmann is a fictional character appearing in the work "The Black Room."
  • 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_69ca83a1eed081908b2e9580f2ebeea7 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cdd05960008190baecb8e4c9f2461f completed April 2, 2026, 2:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2cbe40d088190838819eac97c61e4 completed April 5, 2026, 8:53 p.m.
Created at: March 30, 2026, 9:01 p.m.