Triple

T4719974
Position Surface form Disambiguated ID Type / Status
Subject Negros Island E104739 entity
Predicate hasCity P316 FINISHED
Object Victorias E276008 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: Victorias | Statement: [Negros Island, hasCity, Victorias]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Victorias
Context triple: [Negros Island, hasCity, Victorias]
  • A. Victorias chosen
    Victorias is a city in the Philippine province of Negros Occidental known for its sugar industry and historic Victorias Milling Company.
  • B. Victoria
    Victoria was the Spanish carrack that became the first ship to successfully circumnavigate the globe during Ferdinand Magellan’s expedition.
  • C. Victoria
    Victoria is a central London district known for its major transport hub, theatres, offices, and proximity to landmarks like Buckingham Palace.
  • D. Victoria
    Victoria was the former frequent-flyer loyalty program of TAP Air Portugal, offering mileage accrual and redemption benefits to the airline’s customers.
  • E. Victoria
    Victoria is a coastal municipality in the province of Northern Samar in the Philippines, known for its rural communities and agricultural economy.
  • 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_69bd43ec4a348190bc41afae43375e71 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6428e9e081908ce4041183cad13b completed March 20, 2026, 3:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69be108fe3b08190b3d306ca4b39860d completed March 21, 2026, 3:29 a.m.
Created at: March 20, 2026, 1:18 p.m.