Triple

T6748172
Position Surface form Disambiguated ID Type / Status
Subject Egerton Swartwout E154270 entity
Predicate workLocation P7 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: [Egerton Swartwout, workLocation, Pennsylvania]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pennsylvania
Context triple: [Egerton Swartwout, workLocation, 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 the stage and given name of Penn Jillette, the outspoken magician, comedian, and half of the famed duo Penn & Teller.
  • E. Penn
    Penn is a private Ivy League research university in Philadelphia known for its strong programs in business, law, medicine, and the liberal arts.
  • 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_69c6880ef37881909268a5a7299b9293 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d1d785dc81909c033084d2068568 completed March 27, 2026, 6:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69c723c148c88190bf47495b2d105f73 completed March 28, 2026, 12:41 a.m.
Created at: March 27, 2026, 2:11 p.m.