Triple

T1456321
Position Surface form Disambiguated ID Type / Status
Subject Lower Silesia E31408 entity
Predicate hasMountainRange P651 FINISHED
Object Sudetes E49632 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: Sudetes | Statement: [Lower Silesia, hasMountainRange, Sudetes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sudetes
Context triple: [Lower Silesia, hasMountainRange, Sudetes]
  • A. Sudetes chosen
    The Sudetes are a mountain range in Central Europe spanning parts of Poland, the Czech Republic, and Germany, known for their forested peaks, mineral resources, and popular spa and ski resorts.
  • B. Vianen
    Vianen is a historic Dutch town known for its medieval city center and location near major rivers in the western Netherlands.
  • C. Samogitia
    Samogitia is a historic ethnographic region in northwestern Lithuania known for its distinct Samogitian dialect, strong cultural identity, and late Christianization compared to the rest of Europe.
  • D. Skvyra
    Skvyra is a town in central Ukraine historically known for its significant Jewish community and cultural life in the 19th and early 20th centuries.
  • E. Lielupe
    Lielupe is a major river in central Latvia that flows into the Gulf of Riga and is known for its wide floodplain and role in regional agriculture and transport.
  • 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_69a49917dfc081909acdbdf5d684f1ef completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c581714881909bf4c2bad9645176 completed March 1, 2026, 11:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad0e746b64819091079ff26a60a12e completed March 8, 2026, 5:51 a.m.
Created at: March 1, 2026, 8 p.m.