Triple

T6332194
Position Surface form Disambiguated ID Type / Status
Subject Mount Hope Airport E142405 entity
Predicate hasCityServed P3936 FINISHED
Object Burlington E344391 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: Burlington | Statement: [Mount Hope Airport, hasCityServed, Burlington]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Burlington
Context triple: [Mount Hope Airport, hasCityServed, Burlington]
  • A. Burlington
    Burlington is a suburban town in Massachusetts known for its proximity to Boston and its mix of residential neighborhoods, office parks, and retail centers.
  • B. Burlington
    Burlington is a historic city in present-day New Jersey that once served as the colonial capital of the Province of New Jersey.
  • C. Burlington
    Burlington is a mid-sized city in southern Ontario, Canada, located on the shores of Lake Ontario between Toronto and Hamilton.
  • D. Burlington
    Burlington is a city in North Carolina known historically as a railroad and textile manufacturing hub in the Piedmont region of the state.
  • E. Burlington chosen
    Burlington is a small city in northwestern Washington State known as a commercial hub for the surrounding Skagit Valley region.
  • 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_69c008d4d8e88190ad301c05b08722ac completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c0651634b08190b54860ba0a70f5c4 completed March 22, 2026, 9:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6537fc2ac8190a05779363ed2b3eb completed March 27, 2026, 9:53 a.m.
Created at: March 22, 2026, 4:30 p.m.