Triple

T6927245
Position Surface form Disambiguated ID Type / Status
Subject Sandusky High School E160341 entity
Predicate city P40 FINISHED
Object Sandusky E44706 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: Sandusky | Statement: [Sandusky High School, city, Sandusky]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sandusky
Context triple: [Sandusky High School, city, Sandusky]
  • A. Sandusky, Ohio chosen
    Sandusky, Ohio is a city on the shores of Lake Erie best known as a regional tourist hub and home to the Cedar Point amusement park.
  • B. Hudson, Ohio
    Hudson, Ohio is a small city in northeastern Ohio known for its historic New England–style downtown and as a center of education and culture in the region.
  • C. Ashtabula, Ohio
    Ashtabula, Ohio is a small industrial and port city on Lake Erie in northeastern Ohio, historically known for its shipping industry and diverse immigrant communities.
  • D. Lorain
    Lorain is an industrial city on Lake Erie in northern Ohio, historically known for its steel production and shipbuilding.
  • E. Johnstown, Pennsylvania
    Johnstown, Pennsylvania is a small city in western Pennsylvania best known for its history of devastating floods, particularly the Great Johnstown Flood of 1889.
  • 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_69c6884d350081908d8a970e4d40ad78 completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6da1bf2088190a8ccfa01d9a1efc5 completed March 27, 2026, 7:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69c75859735081909382f1542271a1e4 completed March 28, 2026, 4:26 a.m.
Created at: March 27, 2026, 2:27 p.m.