Triple

T5616424
Position Surface form Disambiguated ID Type / Status
Subject Lakeland Linder International Airport E147487 entity
Predicate nearCity P350 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: [Lakeland Linder International Airport, nearCity, Orlando, Florida]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Orlando, Florida
Context triple: [Lakeland Linder International Airport, nearCity, 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 historic township area within Soweto, South Africa, known for its central role in the anti-apartheid struggle and vibrant local culture.
  • C. Orlando
    Orlando is a common Italian surname borne by numerous individuals, including notable political and cultural figures.
  • D. 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.
  • E. Orlando
    Orlando is the Italian literary counterpart of the medieval knight Roland, best known as the chivalric hero of epic poems such as "Orlando Furioso."
  • 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_69c00905d4588190bd967842bbcf2219 completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c021da7f848190bb1cd0270ad6398f completed March 22, 2026, 5:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69c04d55b95c8190a5f3e2c05249c136 completed March 22, 2026, 8:13 p.m.
Created at: March 22, 2026, 3:39 p.m.