Triple

T9141716
Position Surface form Disambiguated ID Type / Status
Subject Michigan Firehouse Museum E219343 entity
Predicate city P40 FINISHED
Object Ypsilanti E58392 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: Ypsilanti | Statement: [Michigan Firehouse Museum, city, Ypsilanti]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ypsilanti
Context triple: [Michigan Firehouse Museum, city, Ypsilanti]
  • A. Ypsilanti chosen
    Ypsilanti is a city in southeastern Michigan known for Eastern Michigan University and its historic downtown and automotive heritage.
  • B. Livonia
    Livonia is a town in Livingston County, New York, known for its location near Conesus Lake in the Finger Lakes region.
  • C. Livonia
    Livonia was a historical region on the eastern coast of the Baltic Sea, encompassing parts of present-day Latvia and Estonia and long contested by regional powers.
  • D. Rochester Hills
    Rochester Hills is a suburban city in Oakland County, Michigan, known for its affluent residential communities, parks, and proximity to the Detroit metropolitan area.
  • E. Lapeer
    Lapeer is a small city in Michigan known as an administrative and commercial center for the surrounding rural 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_69ca83e012288190a5771058adbaabd2 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cca8f4df5c8190808edfa7c453a51c completed April 1, 2026, 5:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69d6f67c3a0881909f24d85d74e4c061 completed April 9, 2026, 12:44 a.m.
Created at: March 30, 2026, 7:19 p.m.