Triple

T4391818
Position Surface form Disambiguated ID Type / Status
Subject John Fetterman E99380 entity
Predicate represents P129 FINISHED
Object Pennsylvania E13698 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: Pennsylvania | Statement: [John Fetterman, represents, Pennsylvania]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pennsylvania
Context triple: [John Fetterman, represents, Pennsylvania]
  • A. Pennsylvania chosen
    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.
  • B. Pensilvania
    Pensilvania is a municipality and town located in the Caldas Department of Colombia, known for its coffee-growing economy and mountainous Andean landscape.
  • C. Pennsylvania and New Jersey
    Pennsylvania and New Jersey are neighboring U.S. states in the Mid-Atlantic region, separated for much of their length by the Delaware River.
  • D. Penn
    Penn is a private Ivy League research university in Philadelphia known for its strong programs in business, law, medicine, and the liberal arts.
  • E. Penn
    Penn is the stage and given name of Penn Jillette, the outspoken magician, comedian, and half of the famed duo Penn & Teller.
  • 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_69b3454f739481909ff6c28331f0c0b9 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b35285592881909fcdea225a655950 completed March 12, 2026, 11:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5e50ec35481908cf1e1afffda19cb completed March 14, 2026, 10:45 p.m.
Created at: March 12, 2026, 11:19 p.m.