Triple

T3552077
Position Surface form Disambiguated ID Type / Status
Subject Punta Gnifetti E75132 entity
Predicate canton P3942 FINISHED
Object Valais E13342 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: Valais | Statement: [Punta Gnifetti, canton, Valais]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Valais
Context triple: [Punta Gnifetti, canton, Valais]
  • A. Valais chosen
    Valais is a mountainous canton in southwestern Switzerland known for its Alpine scenery, vineyards, and popular ski resorts such as Zermatt and Verbier.
  • B. Vianen
    Vianen is a historic Dutch town known for its medieval city center and location near major rivers in the western Netherlands.
  • C. Ile-Rousse
    Île-Rousse is a coastal town and popular seaside resort in the Balagne region of northern Corsica, known for its red granite islets and sandy beaches.
  • D. Kutaisi
    Kutaisi is one of Georgia’s major cities, historically significant and formerly a capital, located in the western part of the country.
  • E. Seeland region
    The Seeland region is an area in western Switzerland known for its lakes, fertile plains, and intensive agriculture, particularly vegetable farming.
  • 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_69ad85d33c6c819081d5ac1df13b5680 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc05256f081908b8d6a5df917e679 completed March 8, 2026, 6:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69b38bec437c8190b35ca77dc19d441d completed March 13, 2026, 4 a.m.
Created at: March 8, 2026, 3:20 p.m.