Triple

T490809
Position Surface form Disambiguated ID Type / Status
Subject Wales E9984 entity
Predicate capital P234 FINISHED
Object Cardiff E12034 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: Cardiff | Statement: [Wales, capital, Cardiff]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cardiff
Context triple: [Wales, capital, Cardiff]
  • A. Cardiff chosen
    Cardiff is the capital and largest city of Wales, known as a major cultural, commercial, and sporting center with a rich industrial and maritime history.
  • B. Swansea
    Swansea is a coastal city in South Wales known for its maritime heritage, industrial history, and role as a target during World War II air raids.
  • C. Bridgend
    Bridgend is a town and county borough in South Wales, situated roughly midway between Cardiff and Swansea and known historically for its market and industrial heritage.
  • D. Aberystwyth
    Aberystwyth is a historic seaside and university town on the west coast of Wales, known for its promenade, castle ruins, and role as a cultural and administrative center for the region.
  • E. Merthyr Tydfil
    Merthyr Tydfil is a historic industrial town in South Wales that was once a major center of the iron and coal industries during the Industrial Revolution.
  • 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_69a2e802e2908190ab17c9479e0b6412 completed Feb. 28, 2026, 1:05 p.m.
NER Named-entity recognition batch_69a2f0e22a308190b04d12974fd08a38 completed Feb. 28, 2026, 1:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69a4778034fc8190bedcc537fed5cef9 completed March 1, 2026, 5:29 p.m.
Created at: Feb. 28, 2026, 1:12 p.m.