Triple

T18045167
Position Surface form Disambiguated ID Type / Status
Subject 927th Air Refueling Wing E431752 entity
Predicate garrisonLocation P40 FINISHED
Object Tampa NE NERFINISHED

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: Tampa | Statement: [927th Air Refueling Wing, garrisonLocation, Tampa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tampa
Context triple: [927th Air Refueling Wing, garrisonLocation, Tampa]
  • A. Tampa, Florida chosen
    Tampa, Florida is a major city on Florida’s Gulf Coast known for its professional sports teams, port and business center, and role as a key hub in the greater Tampa Bay area.
  • B. Jacksonville
    Jacksonville is a small city in west-central Illinois known for its historic colleges, including Illinois College, and its role as a regional educational and cultural center.
  • C. Jacksonville
    Jacksonville is a small town located in Telfair County in the U.S. state of Georgia.
  • D. Jacksonville
    Jacksonville is a small village located in Athens County in the southeastern region of the U.S. state of Ohio.
  • E. Orlando
    Orlando is a common Italian surname borne by numerous individuals, including notable political and cultural figures.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8b906482481908183315b9ecf9994 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4bff13f488190993445769551c9c2 completed April 19, 2026, 11:43 a.m.
Created at: April 10, 2026, 10:25 a.m.