Triple

T16459255
Position Surface form Disambiguated ID Type / Status
Subject Bartow, Florida E399762 entity
Predicate locatedNear P294 FINISHED
Object Orlando, Florida E11265 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: Orlando, Florida | Statement: [Bartow, Florida, locatedNear, Orlando, Florida]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Orlando, Florida
Context triple: [Bartow, Florida, locatedNear, Orlando, Florida]
  • A. Orlando chosen
    Orlando is a major city in central Florida known for its theme parks, tourism industry, and entertainment attractions.
  • B. Orlando
    Orlando is a 1992 British period fantasy film, based on Virginia Woolf’s novel, in which Tilda Swinton plays an androgynous noble who lives for centuries while changing gender.
  • C. Orlando
    Orlando is the young, virtuous, and romantically idealistic hero of Shakespeare’s comedy "As You Like It," known for his love for Rosalind and his conflict with his elder brother.
  • D. Orlando
    Orlando is the middle name of William O. Butler, a 19th-century American military officer and politician.
  • E. Orlando
    Orlando is a historic township area within Soweto, South Africa, known for its central role in the anti-apartheid struggle and vibrant local culture.
  • 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_69d87f2dac988190b74d6e185fa88ba4 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e32d7ff0e881909100bb5b33c04291 completed April 18, 2026, 7:06 a.m.
NED1 Entity disambiguation (via context triple) batch_6a004f41703c81908fb040a9107045ae completed May 10, 2026, 9:26 a.m.
Created at: April 10, 2026, 5:10 a.m.