Triple

T19203019
Position Surface form Disambiguated ID Type / Status
Subject Sylvania Schools E480155 entity
Predicate county P75 FINISHED
Object Lucas County 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: Lucas County | Statement: [Sylvania Schools, county, Lucas County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lucas County
Context triple: [Sylvania Schools, county, Lucas County]
  • A. Lucas County chosen
    Lucas County is a county in northwestern Ohio that includes the city of Toledo as its county seat and largest urban center.
  • B. Huron County
    Huron County is a predominantly rural county in southwestern Ontario, Canada, known for its agriculture, small towns, and Lake Huron shoreline.
  • C. Huron County
    Huron County is a county in northern Ohio known for its mix of small cities, rural communities, and agricultural land.
  • D. Stark County
    Stark County is a county in northeastern Ohio known for its seat in Canton and its role in the region’s industrial and political history.
  • E. Stark County
    Stark County is a county in southwestern North Dakota known for its seat in Dickinson and its role as a regional hub for agriculture, energy, and transportation.
  • 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_69d8e8cb8c348190b52075823911c869 completed April 10, 2026, 12:10 p.m.
NER Named-entity recognition batch_69e5f99a571c8190a1d53eb1994e0058 completed April 20, 2026, 10:02 a.m.
Created at: April 10, 2026, 1:13 p.m.