Triple

T3565373
Position Surface form Disambiguated ID Type / Status
Subject Monroe Regional Airport E75434 entity
Predicate owner P347 FINISHED
Object City of Monroe E255595 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: City of Monroe | Statement: [Monroe Regional Airport, owner, City of Monroe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City of Monroe
Context triple: [Monroe Regional Airport, owner, City of Monroe]
  • A. City of Monroe chosen
    The City of Monroe is a municipal government in northeastern Louisiana that serves as the administrative and cultural center of the Monroe metropolitan area.
  • B. Monroe, Louisiana
    Monroe, Louisiana is a city in northeastern Louisiana that serves as a regional hub for commerce, education, and culture along the Ouachita River.
  • C. West Monroe, Louisiana
    West Monroe, Louisiana is a small city in northeastern Louisiana, located across the Ouachita River from Monroe and known for its shared regional economy and community with its larger twin city.
  • D. Bay City
    Bay City is a small industrial and port city in east-central Michigan located near Saginaw Bay on Lake Huron.
  • E. City of DeSoto
    The City of DeSoto is a suburban municipality in North Texas, located in Dallas County and forming part of the Dallas–Fort Worth metropolitan area.
  • 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_69ad85d512708190829c8b2d3a2ccfb8 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc0a8f6288190928479f5bea32245 completed March 8, 2026, 6:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69b402e8628c81909913974602782b7a completed March 13, 2026, 12:28 p.m.
Created at: March 8, 2026, 3:21 p.m.