Triple

T18655659
Position Surface form Disambiguated ID Type / Status
Subject Dragon Mountains E456058 entity
Predicate contains P35 FINISHED
Object Champagne Castle NE NERFINISHED

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: Champagne Castle | Statement: [Dragon Mountains, contains, Champagne Castle]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Champagne Castle
Context triple: [Dragon Mountains, contains, Champagne Castle]
  • A. Champagne Castle chosen
    Champagne Castle is a prominent mountain peak in South Africa’s Drakensberg range, known for its dramatic cliffs and popular hiking routes.
  • B. Château de Brienne
    Château de Brienne is a historic French castle in the town of Brienne-le-Château, notably associated with Napoleon Bonaparte’s early military education.
  • C. Valère Castle
    Valère Castle is a historic fortified complex overlooking the town of Sion in the Swiss canton of Valais, notable for its medieval architecture and hilltop basilica.
  • D. Clisson Castle
    Clisson Castle is a medieval fortress in western France notable as the birthplace of Francis II, the last independent Duke of Brittany.
  • E. Walferdange Castle
    Walferdange Castle is a historic Luxembourgish residence best known as the place where Prince Henry of the Netherlands died in 1879.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8d38ea1e88190997e9b231190ba6f completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e55084ca3481909ff3fd9045f25dcd completed April 19, 2026, 10 p.m.
Created at: April 10, 2026, 11:47 a.m.