Triple

T7754293
Position Surface form Disambiguated ID Type / Status
Subject Kings Billabong Park E175850 entity
Predicate state P87 FINISHED
Object Victoria E145661 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: Victoria | Statement: [Kings Billabong Park, state, Victoria]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Victoria
Context triple: [Kings Billabong Park, state, Victoria]
  • A. Victoria
    Victoria is a coastal municipality in the province of Northern Samar in the Philippines, known for its rural communities and agricultural economy.
  • B. Victoria
    Victoria is a vengeful vampire antagonist from the Twilight series who relentlessly hunts Bella Swan and opposes the Cullen family.
  • C. Victoria
    Victoria was the eldest child of Queen Victoria and Prince Albert, who became German Empress and Queen of Prussia through her marriage to Frederick III.
  • D. 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.
  • E. Victoria
    Victoria is a Canadian national honour recognizing outstanding achievement and service, associated with the motto of the Order of Canada (CVO).
  • 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_69c6996180088190832e38e8d83ff54a completed March 27, 2026, 2:51 p.m.
NER Named-entity recognition batch_69c703d851d4819091e9117d3f34cb9a completed March 27, 2026, 10:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8c7c12e0081908eb984ba9bc558ff completed March 29, 2026, 6:33 a.m.
Created at: March 27, 2026, 4:08 p.m.