Triple

T756419
Position Surface form Disambiguated ID Type / Status
Subject Mapp v. Ohio E15566 entity
Predicate respondent P2238 FINISHED
Object State of Ohio E30904 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: State of Ohio | Statement: [Mapp v. Ohio, respondent, State of Ohio]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: State of Ohio
Context triple: [Mapp v. Ohio, respondent, State of Ohio]
  • A. Ohio chosen
    Ohio is a Midwestern U.S. state known for its diverse economy, major cities like Columbus, Cleveland, and Cincinnati, and its significant role in national politics as a historic swing state.
  • B. Indiana
    Indiana is a U.S. state known for its manufacturing base, rich agricultural land, and iconic events like the Indianapolis 500.
  • C. Pennsylvania
    Pennsylvania is a historically significant U.S. state in the Mid-Atlantic and Northeastern regions, known for cities like Philadelphia and Pittsburgh and its central role in the nation’s founding.
  • D. Maryland
    Maryland is a Mid-Atlantic U.S. state known for its Chesapeake Bay shoreline, colonial history, and proximity to the nation’s capital.
  • E. Illinois
    Illinois is a Midwestern U.S. state known for its major metropolis Chicago, diverse economy, and significant political and transportation influence.
  • 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_69a493599a0081908da65f3407af1ef2 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a66ab4608190afcd81e6606c5116 completed March 1, 2026, 8:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad3072c3a881908c33159cdd55ae0b completed March 8, 2026, 8:16 a.m.
Created at: March 1, 2026, 7:37 p.m.