Triple

T21819130
Position Surface form Disambiguated ID Type / Status
Subject Museums Victoria E538676 entity
Predicate state P87 FINISHED
Object Victoria NE NERFINISHED

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: Victoria | Statement: [Museums Victoria, state, Victoria]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Victoria
Context triple: [Museums Victoria, state, Victoria]
  • A. Victoria
    Victoria is a vengeful vampire antagonist from the Twilight series who relentlessly hunts Bella Swan and opposes the Cullen family.
  • B. Victoria
    Victoria is the birth name of American actress and model Tanya Roberts, known for her roles in "Charlie's Angels" and the James Bond film "A View to a Kill."
  • C. Victoria chosen
    Victoria is a coastal city on the southern tip of Vancouver Island known for its historic architecture, mild climate, and vibrant tourism industry.
  • D. Victoria
    Victoria is a former WWE wrestler best known for her powerful in-ring style and prominent role in the women's division during the Ruthless Aggression era.
  • E. Victoria
    Victoria is a traditional design style used in Basotho blankets, recognized for its distinctive patterns and cultural significance in Lesotho.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0c475038c8190abb9b1a20eb8ff50 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69f07cce0b8081909e20ded72db40304 completed April 28, 2026, 9:24 a.m.
Created at: April 16, 2026, 6:54 p.m.