Triple

T3067112
Position Surface form Disambiguated ID Type / Status
Subject Tom Stoppard E62129 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: [Tom Stoppard, placeOfBirth, Zlín]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zlín
Context triple: [Tom Stoppard, 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. Znojmo
    Znojmo is a historic town in the South Moravian Region of the Czech Republic, known for its medieval architecture, wine production, and strategic position near the Austrian border.
  • C. Karviná
    Karviná is an industrial city in the Moravian-Silesian Region of the Czech Republic, historically part of Cieszyn Silesia and known for its coal mining heritage.
  • D. Plzeň
    Plzeň is a major city in western Bohemia in the Czech Republic, known for its brewing tradition and industrial heritage.
  • E. Žilina
    Žilina is a city in northwestern Slovakia that serves as an important industrial and transportation hub, particularly for rail connections in the region.
  • 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_69ad85793e5c8190a358049bc4a98d8c completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada0fea06881909e5251eea26599ac completed March 8, 2026, 4:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69b373888b948190b84d7bfa908f15ad completed March 13, 2026, 2:16 a.m.
Created at: March 8, 2026, 3:02 p.m.