Triple

T19599145
Position Surface form Disambiguated ID Type / Status
Subject Orangetown E470421 entity
Predicate contains P35 FINISHED
Object Orangeburg 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: Orangeburg | Statement: [Orangetown, contains, Orangeburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Orangeburg
Context triple: [Orangetown, contains, Orangeburg]
  • A. Orangeburg, South Carolina chosen
    Orangeburg, South Carolina is a small city in central South Carolina known for its historic roots, regional agriculture, and as the home of South Carolina State University and Claflin University.
  • B. Greenville
    Greenville is a residential neighborhood in the southern part of Jersey City, New Jersey, known for its diverse community and urban character.
  • C. Greenville
    Greenville is a mid-sized city in the northwestern part of South Carolina known for its revitalized downtown, cultural amenities, and role as an economic hub in the Upstate region.
  • D. Greenville
    Greenville is a small city in southern Illinois known for its historic downtown and as the home of Greenville University.
  • E. Greenville
    Greenville is a small city in south-central Alabama known for its historic downtown and role as the county seat of Butler County.
  • 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_69d8e510024481908415c0d616fa6186 completed April 10, 2026, 11:54 a.m.
NER Named-entity recognition batch_69e6407d46188190b9818665b2a698a5 completed April 20, 2026, 3:04 p.m.
Created at: April 10, 2026, 1:43 p.m.