Triple

T9929846
Position Surface form Disambiguated ID Type / Status
Subject Borough of McSherrystown E192617 entity
Predicate hasPostalCountry P846 FINISHED
Object US E391540 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: US | Statement: [Borough of McSherrystown, hasPostalCountry, US]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: US
Context triple: [Borough of McSherrystown, hasPostalCountry, US]
  • A. US chosen
    The US, or United States, is a federal republic in North America comprising 50 states and known as one of the world's largest economic and military powers.
  • B. US
    The US, or United States, is a large federal republic in North America composed of 50 states and known as one of the world's most influential economic and political powers.
  • C. US
    The US, or United States, is a large federal republic in North America composed of 50 states and known as a major global economic and political power.
  • D. US
    US is the commonly used abbreviation for the University of Szczecin, a public higher education institution in Szczecin, Poland.
  • E. US
    US is the commonly used abbreviation for the University of Seville, a major public research university located in Seville, Spain.
  • 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_69ca82dd978c8190947124ab0d3315ac completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cdb5b215c481909e0bca43f158bd82 completed April 2, 2026, 12:17 a.m.
NED1 Entity disambiguation (via context triple) batch_69d20cbe7fb88190a945870540d4c973 completed April 5, 2026, 7:18 a.m.
Created at: March 30, 2026, 8:43 p.m.