Triple

T10571013
Position Surface form Disambiguated ID Type / Status
Subject Clwyd South E249478 entity
Predicate borderedBy P224 FINISHED
Object Montgomeryshire E13915 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: Montgomeryshire | Statement: [Clwyd South, borderedBy, Montgomeryshire]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Montgomeryshire
Context triple: [Clwyd South, borderedBy, Montgomeryshire]
  • A. Montgomeryshire chosen
    Montgomeryshire is a historic county and former parliamentary constituency in mid-Wales, known for its rural landscape and market towns such as Newtown and Welshpool.
  • B. Glamorganshire
    Glamorganshire was a historic county in south Wales that included industrial towns, rural areas, and much of what is now the modern county of Glamorgan.
  • C. Carmarthenshire
    Carmarthenshire is a largely rural county in southwest Wales known for its market towns, rich agricultural land, and scenic landscapes stretching from the Tywi Valley to the Carmarthen Bay coast.
  • D. Monmouthshire
    Monmouthshire is a historic county and principal area in southeast Wales, known for its rural landscapes, market towns, and rich medieval heritage.
  • E. Powys
    Powys is a large, predominantly rural county in mid-Wales known for its mountainous landscapes, market towns, and extensive agricultural areas.
  • 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_69d381c8bd708190acf3d275c908251e completed April 6, 2026, 9:50 a.m.
NER Named-entity recognition batch_69d5274678148190b7a1afc099628357 completed April 7, 2026, 3:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69e50983ce848190ab375145019ff69b completed April 19, 2026, 4:57 p.m.
Created at: April 6, 2026, 12:37 p.m.