Triple

T2409298
Position Surface form Disambiguated ID Type / Status
Subject Franklin County, Ohio E50348 entity
Predicate contains P35 FINISHED
Object Bexley, Ohio
Bexley, Ohio is a small, affluent suburban city near downtown Columbus known for its historic homes, tree-lined streets, and institutions like Capital University.
 E312778 NE FINISHED

How this triple was built (4 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: Bexley, Ohio | Statement: [Franklin County, Ohio, contains, Bexley, Ohio]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bexley, Ohio
Context triple: [Franklin County, Ohio, contains, Bexley, Ohio]
  • A. Bellevue, Ohio
    Bellevue, Ohio is a small city in north-central Ohio known for its railroad heritage and location spanning multiple counties.
  • B. Bryan, Ohio
    Bryan, Ohio is a small city in northwestern Ohio that serves as the county seat of Williams County.
  • C. Englewood, Ohio
    Englewood, Ohio is a suburban city in Montgomery County that forms part of the Dayton metropolitan area in southwestern Ohio.
  • D. Xenia, Ohio
    Xenia, Ohio is a small city in southwestern Ohio known for its historic downtown, proximity to Dayton, and extensive network of bike trails.
  • E. Miamisburg, Ohio
    Miamisburg, Ohio is a suburban city in southwestern Ohio known for its historic downtown and proximity to Dayton in the Miami Valley region.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Bexley, Ohio
Triple: [Franklin County, Ohio, contains, Bexley, Ohio]
Generated description
Bexley, Ohio is a small, affluent suburban city near downtown Columbus known for its historic homes, tree-lined streets, and institutions like Capital University.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Bexley, Ohio
Target entity description: Bexley, Ohio is a small, affluent suburban city near downtown Columbus known for its historic homes, tree-lined streets, and institutions like Capital University.
  • A. Bellevue, Ohio
    Bellevue, Ohio is a small city in north-central Ohio known for its railroad heritage and location spanning multiple counties.
  • B. Bryan, Ohio
    Bryan, Ohio is a small city in northwestern Ohio that serves as the county seat of Williams County.
  • C. Englewood, Ohio
    Englewood, Ohio is a suburban city in Montgomery County that forms part of the Dayton metropolitan area in southwestern Ohio.
  • D. Xenia, Ohio
    Xenia, Ohio is a small city in southwestern Ohio known for its historic downtown, proximity to Dayton, and extensive network of bike trails.
  • E. Miamisburg, Ohio
    Miamisburg, Ohio is a suburban city in southwestern Ohio known for its historic downtown and proximity to Dayton in the Miami Valley region.
  • F. None of above. chosen

Provenance (5 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_69a88b0339a88190a1207333cd271cc9 completed March 4, 2026, 7:41 p.m.
NER Named-entity recognition batch_69abc925c6e481909bfd45b361d21963 completed March 7, 2026, 6:43 a.m.
NED1 Entity disambiguation (via context triple) batch_69b08623c8188190b48615107b653b60 completed March 10, 2026, 8:59 p.m.
NEDg Description generation batch_69b0cf4aec348190bd23af013c5e90ba completed March 11, 2026, 2:11 a.m.
NED2 Entity disambiguation (via description) batch_69b0cfb38b788190a916b5c723bd804d completed March 11, 2026, 2:13 a.m.
Created at: March 4, 2026, 7:58 p.m.