Triple

T9612124
Position Surface form Disambiguated ID Type / Status
Subject Cleveland metropolitan area E232126 entity
Predicate hasPrincipalCity P3940 FINISHED
Object Elyria E274286 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: Elyria | Statement: [Cleveland metropolitan area, hasPrincipalCity, Elyria]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Elyria
Context triple: [Cleveland metropolitan area, hasPrincipalCity, Elyria]
  • A. Elyria, Ohio chosen
    Elyria, Ohio is a city in Lorain County that serves as a residential and commercial hub within the Greater Cleveland area of northern Ohio.
  • B. Havana, Ohio
    Havana, Ohio is a small unincorporated community located in Huron County in north-central Ohio.
  • C. Dresden, Ohio
    Dresden, Ohio is a small village in Muskingum County known historically as the original home of the Longaberger Company and its handcrafted baskets.
  • D. Wapakoneta, Ohio
    Wapakoneta, Ohio is a small city in western Ohio best known as the hometown of astronaut Neil Armstrong and for its strong ties to aerospace history.
  • E. Lorain
    Lorain is an industrial city on Lake Erie in northern Ohio, historically known for its steel production and shipbuilding.
  • 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_69ca8485a90c819094fe40b42fde9d70 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9a87764481909ab96cd2ab96d14b completed April 1, 2026, 10:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69d95e38789881909e45e8d0b0489a59 completed April 10, 2026, 8:31 p.m.
Created at: March 30, 2026, 8:09 p.m.