Triple

T1841122
Position Surface form Disambiguated ID Type / Status
Subject Norman E41178 entity
Predicate county P75 FINISHED
Object Cleveland County E291656 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: Cleveland County | Statement: [Norman, county, Cleveland County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cleveland County
Context triple: [Norman, county, Cleveland County]
  • A. Cleveland County
    Cleveland County is a county in southwestern North Carolina that forms part of the greater Charlotte metropolitan region.
  • B. Cleveland County chosen
    Cleveland County is a county in central Oklahoma that forms part of the Oklahoma City metropolitan area.
  • C. Madison County
    Madison County is a county in central New York State known for its rural communities, agriculture, and small historic towns such as Oneida and Cazenovia.
  • D. Madison County
    Madison County is a county in central Mississippi, located in the Jackson metropolitan area and known for its rapidly growing suburban communities.
  • E. Madison County
    Madison County is a county in northern Alabama that includes the city of Huntsville, a major center for aerospace, defense, and technology.
  • 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_69a88647f9388190909bc36e795bdaec completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb03c5e8081909578eaba8d82c264 completed March 7, 2026, 4:57 a.m.
NED1 Entity disambiguation (via context triple) batch_69b35449d84c819082e1a1aefc7234c5 completed March 13, 2026, 12:03 a.m.
Created at: March 4, 2026, 7:33 p.m.