Triple

T755920
Position Surface form Disambiguated ID Type / Status
Subject Tump Terret motte E15555 entity
Predicate locatedIn P40 FINISHED
Object Monmouthshire E27493 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: Monmouthshire | Statement: [Tump Terret motte, locatedIn, Monmouthshire]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Monmouthshire
Context triple: [Tump Terret motte, locatedIn, Monmouthshire]
  • A. Monmouthshire chosen
    Monmouthshire is a historic county and principal area in southeast Wales, known for its rural landscapes, market towns, and rich medieval heritage.
  • B. Montgomeryshire
    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.
  • C. Denbighshire
    Denbighshire is a historic and principal county in north-east Wales, known for its rural landscapes, market towns, and sections of the Clwydian Range and Dee Valley Area of Outstanding Natural Beauty.
  • D. Pembrokeshire
    Pembrokeshire is a coastal county in southwest Wales renowned for its rugged cliffs, sandy beaches, and the Pembrokeshire Coast National Park.
  • 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_69a493599a0081908da65f3407af1ef2 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a6693cbc8190a167e12a896d7ce7 completed March 1, 2026, 8:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7edf8ecec819081ce513fcf8a6d11 completed March 4, 2026, 8:31 a.m.
Created at: March 1, 2026, 7:37 p.m.