Triple

T21467775
Position Surface form Disambiguated ID Type / Status
Subject Talfer E529636 entity
Predicate hasNameInItalian P17612 FINISHED
Object Talvera 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: Talvera | Statement: [Talfer, hasNameInItalian, Talvera]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Talvera
Context triple: [Talfer, hasNameInItalian, Talvera]
  • A. Talvera chosen
    Talvera is a river in northern Italy that flows through South Tyrol and joins the Adige near the city of Bolzano.
  • B. Avilés
    Avilés is a historic coastal city in northern Spain’s Asturias region, known for its medieval old town and long maritime and industrial heritage.
  • C. Valverde de la Vera
    Valverde de la Vera is a historic village in the province of Cáceres, Extremadura, Spain, known for its traditional architecture and scenic setting in the La Vera region.
  • D. Villalba
    Villalba is a frazione (hamlet) of the municipality of Guidonia Montecelio in the Lazio region of central Italy.
  • E. Villalba
    Villalba is a small town and comune in central Sicily, Italy, known for its agricultural economy and location within the Province of Caltanissetta.
  • 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_69e0c459acb481909bb6ee452a0045c7 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69e9e9f415d48190a2b0993a4f3c018f completed April 23, 2026, 9:44 a.m.
Created at: April 16, 2026, 6:15 p.m.