Triple

T1013615
Position Surface form Disambiguated ID Type / Status
Subject Ivana Marie Zelníčková E21879 entity
Predicate placeOfBirth P1 FINISHED
Object Zlín E23399 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: Zlín | Statement: [Ivana Marie Zelníčková, placeOfBirth, Zlín]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zlín
Context triple: [Ivana Marie Zelníčková, placeOfBirth, Zlín]
  • A. Zlín chosen
    Zlín is a city in the Czech Republic known for its modernist architecture and historical association with the Baťa shoe company.
  • B. Plzeň
    Plzeň is a major city in western Bohemia in the Czech Republic, known for its brewing tradition and industrial heritage.
  • C. Trenčín
    Trenčín is a historic city in western Slovakia known for its medieval castle overlooking the Váh River and its role as a regional cultural and economic center.
  • D. Ostrava
    Ostrava is a major industrial and cultural city in the northeastern Czech Republic, near the borders with Poland and Slovakia.
  • E. Hradec Králové
    Hradec Králové is a historic city in the Czech Republic known for its educational institutions, modernist architecture, and role as a regional cultural and economic center.
  • 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_69a493c68e24819080ed0ee8bcfd5ce0 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b7a8b254819089ffed9cb62a6930 completed March 1, 2026, 10:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69acddfeb918819083f948183aaaeb09 completed March 8, 2026, 2:25 a.m.
Created at: March 1, 2026, 7:41 p.m.