Triple

T4366507
Position Surface form Disambiguated ID Type / Status
Subject United States Attorney for the Southern District of Ohio E98785 entity
Predicate officeLocation P40 FINISHED
Object Dayton, Ohio E152840 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: Dayton, Ohio | Statement: [United States Attorney for the Southern District of Ohio, officeLocation, Dayton, Ohio]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dayton, Ohio
Context triple: [United States Attorney for the Southern District of Ohio, officeLocation, Dayton, Ohio]
  • A. Dayton
    Dayton is an unincorporated community and census-designated place located within South Brunswick Township in Middlesex County, New Jersey.
  • B. Dayton
    Dayton is a mid-sized city in southwestern Ohio known for its historic role in aviation, manufacturing, and research, including its close association with major U.S. Air Force installations.
  • C. Dayton
    Dayton is a masculine given name of English origin used both as a first name and a surname.
  • D. Dayton
    Dayton is a small city in Minnesota known for its suburban-rural character and location within the Minneapolis–Saint Paul metropolitan area.
  • E. Dayton, Ohio, United States chosen
    Dayton is a mid-sized city in southwestern Ohio known historically as a center of aviation innovation and manufacturing.
  • 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_69b3454db3708190aeafd814413c4c3d completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b35200263081909bb326a4d7a8db99 completed March 12, 2026, 11:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69be43821d5c8190b50e3eadabf2845b completed March 21, 2026, 7:06 a.m.
Created at: March 12, 2026, 11:17 p.m.