Triple

T21337416
Position Surface form Disambiguated ID Type / Status
Subject Tychy E526085 entity
Predicate hasTwinTown P919 FINISHED
Object Cassino 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: Cassino | Statement: [Tychy, hasTwinTown, Cassino]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cassino
Context triple: [Tychy, hasTwinTown, Cassino]
  • A. Cassino chosen
    Cassino is a town in central Italy known for its strategic location and the nearby Monte Cassino, site of a historic Benedictine abbey and major World War II battles.
  • B. Cassino
    Cassino is a locality within the Italian municipality of Cannobio, situated in the Piedmont region near Lake Maggiore.
  • C. Seregno
    Seregno is a town in the Lombardy region of northern Italy, known for its industrial activity and proximity to Milan.
  • D. Castel di Sangro
    Castel di Sangro is a historic town in Italy’s Abruzzo region, known for its scenic Apennine mountain setting and as the subject of the football book "The Miracle of Castel di Sangro."
  • E. Campogalliano
    Campogalliano is a small Italian town and municipality in the Emilia-Romagna region, known for its industrial activity and proximity to the city of Modena.
  • 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_69e0b51c33048190ab27cede74ef798c completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e898da015081909e83fb62cf166b9a completed April 22, 2026, 9:46 a.m.
Created at: April 16, 2026, 4:43 p.m.