Triple

T11763766
Position Surface form Disambiguated ID Type / Status
Subject Arfon E279725 entity
Predicate locatedIn P40 FINISHED
Object Gwynedd E58308 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: Gwynedd | Statement: [Arfon, locatedIn, Gwynedd]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gwynedd
Context triple: [Arfon, locatedIn, Gwynedd]
  • A. Gwynedd chosen
    Gwynedd is a county and preserved lieutenancy area in north-west Wales known for its rugged coastline, mountains including much of Snowdonia, and strong Welsh language and cultural heritage.
  • B. Caernarfonshire
    Caernarfonshire is a historic county in northwest Wales known for its rugged coastline, mountainous landscapes including parts of Snowdonia, and the medieval Caernarfon Castle.
  • C. Ceredigion
    Ceredigion is a coastal county in west Wales known for its rugged Cardigan Bay shoreline, rural landscapes, and Welsh-speaking communities.
  • D. Merionethshire
    Merionethshire is a historic county in northwest Wales known for its rugged mountainous landscapes and rural character.
  • 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_69d6ab01d2688190ad8ed6bda487eaa5 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a5248e0881909ed1b4df7be422f7 completed April 10, 2026, 7:22 a.m.
NED1 Entity disambiguation (via context triple) batch_69f416d18eec81909f417e49507b117d completed May 1, 2026, 2:58 a.m.
Created at: April 8, 2026, 9:41 p.m.