Triple

T5464748
Position Surface form Disambiguated ID Type / Status
Subject Richard Hodges E122679 entity
Predicate workLocation P7 FINISHED
Object 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: Ohio | Statement: [Richard Hodges, workLocation, Ohio]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ohio
Context triple: [Richard Hodges, workLocation, 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. Michigan
    Michigan is a U.S. state in the Great Lakes region known for its extensive freshwater coastline, automotive industry centered in Detroit, and diverse natural landscapes.
  • D. Michigan
    Michigan is a U.S. state in the Great Lakes region known for its extensive freshwater coastline, automotive industry heritage, and diverse forests and waterways.
  • E. Michigan
    Michigan is a U.S. state in the Great Lakes region known for its automotive industry, extensive freshwater coastline, and divided Upper and Lower Peninsulas.
  • 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_69bd4643f16081908d7f29e08096115a completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd920590b481909b92091678ff1414 completed March 20, 2026, 6:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf415146988190823ab77b091231a1 completed March 22, 2026, 1:09 a.m.
Created at: March 20, 2026, 2:08 p.m.