Triple

T20224806
Position Surface form Disambiguated ID Type / Status
Subject KPKB E495349 entity
Predicate servesCity P82 FINISHED
Object Parkersburg NE NERFINISHED

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: Parkersburg | Statement: [KPKB, servesCity, Parkersburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Parkersburg
Context triple: [KPKB, servesCity, Parkersburg]
  • A. Parkersburg, West Virginia chosen
    Parkersburg, West Virginia is a mid-sized city along the Ohio River that serves as a regional economic and transportation hub in western West Virginia.
  • B. Petersburg, West Virginia
    Petersburg, West Virginia, is a small city in the Potomac Highlands region known as a local commercial hub and gateway to nearby outdoor recreation areas such as Dolly Sods and Seneca Rocks.
  • C. Clarksburg
    Clarksburg is a small town located in the northern part of Massachusetts, near the Vermont border, known for its rural character and natural scenery.
  • D. Morganstown
    Morganstown is a village in Cardiff, Wales, situated near Radyr and forming part of the city's northern suburban area.
  • E. Waynesborough
    Waynesborough is the historic Pennsylvania estate that served as the ancestral home of the prominent Wayne family, including Revolutionary War General Anthony Wayne.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69da626cff80819097b530718a7c98b6 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e66fd8f1948190adbb947a7870bb43 completed April 20, 2026, 6:26 p.m.
Created at: April 11, 2026, 11:39 p.m.