Triple

T21372302
Position Surface form Disambiguated ID Type / Status
Subject Geldern E527096 entity
Predicate hasTwinTown P919 FINISHED
Object Fivizzano 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: Fivizzano | Statement: [Geldern, hasTwinTown, Fivizzano]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Fivizzano
Context triple: [Geldern, hasTwinTown, Fivizzano]
  • A. Fivizzano chosen
    Fivizzano is a historic town and comune in the province of Massa-Carrara in Tuscany, central Italy, known for its medieval architecture and scenic Apennine surroundings.
  • B. Vicopisano
    Vicopisano is a historic Tuscan town in central Italy known for its medieval architecture and scenic setting near the Arno River.
  • C. Monterubbiano
    Monterubbiano is a historic hilltop town in Italy’s Marche region, known for its medieval architecture and panoramic views over the surrounding countryside.
  • D. Fiuggi
    Fiuggi is an Italian spa town in the Lazio region, renowned for its mineral water springs and therapeutic treatments.
  • E. Vinza
    Vinza is a locality associated with the Rwanda-Rundi–speaking region of East Africa, likely a village or small community within that cultural-linguistic area.
  • 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_69e0b51e80808190ba5cb05667af02a9 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e8b0b0d5ec81908da8f38380dbdc7a completed April 22, 2026, 11:27 a.m.
Created at: April 16, 2026, 5:10 p.m.