Triple

T14550046
Position Surface form Disambiguated ID Type / Status
Subject Florence, Arizona E341391 entity
Predicate namedAfter P63 FINISHED
Object Florence, Italy E26762 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: Florence, Italy | Statement: [Florence, Arizona, namedAfter, Florence, Italy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Florence, Italy
Context triple: [Florence, Arizona, namedAfter, 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 an unincorporated community within Florence Township in New Jersey, known primarily as a residential area along the Delaware River.
  • D. Florence
    Florence is a small town in Fremont County, Colorado, known for its historic downtown and role as a gateway to nearby outdoor attractions.
  • E. 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.
  • 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_69d822db9c8481908213ceb39585f792 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb2ed2b4c8190945bd26531c71f1f completed April 14, 2026, 9:34 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd94afc95c8190ae4aff12c9d69c88 completed May 8, 2026, 7:45 a.m.
Created at: April 10, 2026, 1:23 a.m.