Triple

T1003540
Position Surface form Disambiguated ID Type / Status
Subject Lake Buena Vista, Florida E21656 entity
Predicate region P40 FINISHED
Object Greater Orlando 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: Greater Orlando | Statement: [Lake Buena Vista, Florida, region, Greater Orlando]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Greater Orlando
Context triple: [Lake Buena Vista, Florida, region, Greater Orlando]
  • A. Orlando chosen
    Orlando is a major city in central Florida known for its theme parks, tourism industry, and entertainment attractions.
  • B. Kissimmee, Florida
    Kissimmee, Florida is a central Florida city in Osceola County known for its proximity to major Orlando-area theme parks and tourist attractions.
  • C. Altadena
    Altadena is an unincorporated community in Los Angeles County, California, located in the foothills of the San Gabriel Mountains just north of Pasadena.
  • D. Tallahassee metropolitan area
    The Tallahassee metropolitan area is a regional urban and economic hub in northern Florida centered on the state capital and its surrounding communities.
  • E. Lakeland
    Lakeland is a residential neighborhood located within the city of College Park in Prince George's County, Maryland.
  • 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_69a493c53e648190ae8cb76c433fd9a7 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b4fe0a548190aee8abf1890e141e completed March 1, 2026, 9:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac2a1ecddc8190b954d16c6e269498 completed March 7, 2026, 1:37 p.m.
Created at: March 1, 2026, 7:41 p.m.