Triple

T17243529
Position Surface form Disambiguated ID Type / Status
Subject Zala County E418562 entity
Predicate hasMajorCity P316 FINISHED
Object Nagykanizsa E337046 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: Nagykanizsa | Statement: [Zala County, hasMajorCity, Nagykanizsa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nagykanizsa
Context triple: [Zala County, hasMajorCity, Nagykanizsa]
  • A. Nagykanizsa chosen
    Nagykanizsa is a city in southwestern Hungary known historically as a regional commercial and cultural center.
  • B. Κανὰ
    Κανὰ is the Greek name for Cana, the Galilean village traditionally associated with Jesus’ first miracle of turning water into wine.
  • C. Bighorn Canyon
    Bighorn Canyon is a dramatic, steep-walled gorge carved by the Bighorn River, renowned for its striking desert-and-cliff landscapes and recreational opportunities in the Bighorn Canyon National Recreation Area.
  • D. Boulder Canyon
    Boulder Canyon is a rugged river gorge on the Colorado River in the American Southwest, historically significant in early plans for dam and hydroelectric development in the region.
  • E. Canyon
    Canyon is a 1959 abstract expressionist painting by Helen Frankenthaler, known for its innovative soak-stain technique and luminous color fields.
  • 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_69d886d8e96081909870bff6c3d0bf09 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e42e21bb5c8190ad960f231fe54665 completed April 19, 2026, 1:21 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0170f388608190b709b1c228a7ba29 completed May 11, 2026, 6:02 a.m.
Created at: April 10, 2026, 5:39 a.m.