Triple

T20686552
Position Surface form Disambiguated ID Type / Status
Subject King Kamehameha I statue E508433 entity
Predicate castIn P9534 FINISHED
Object Florence, Italy 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: Florence, Italy | Statement: [King Kamehameha I statue, castIn, Florence, Italy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Florence, Italy
Context triple: [King Kamehameha I statue, castIn, Florence, Italy]
  • A. Florence chosen
    Florence is a historic Italian city renowned as the cradle of the Renaissance, celebrated for its art, architecture, and cultural influence.
  • B. Florence
    Florence is a small coastal city in western Oregon known for its scenic beaches, sand dunes, and historic Old Town along the Siuslaw River.
  • C. Florence
    Florence is a critically acclaimed interactive story and mobile video game that explores the emotional journey of a young woman's first love through minimalist gameplay and visual storytelling.
  • D. Florence
    Florence is an unincorporated community within Florence Township in New Jersey, known primarily as a residential area along the Delaware River.
  • E. Florence
    Florence is a kind, sensible young girl and one of the main human characters in the classic stop-motion children's television series "The Magic Roundabout."
  • 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_69e0b4c1ed408190b72dd26b1e33f8a1 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6beacc30081908e57cd7f2c3ef047 completed April 21, 2026, 12:02 a.m.
Created at: April 16, 2026, 11:45 a.m.