Triple

T28972866
Position Surface form Disambiguated ID Type / Status
Subject České Budějovice District E734321 entity
Predicate containsAdministrativeTerritorialEntity P747 FINISHED
Object Dolní Bukovsko
Dolní Bukovsko is a market town in the South Bohemian Region of the Czech Republic known for its rural character and historical architecture.
E1843439 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: Dolní Bukovsko | Statement: [České Budějovice District, containsAdministrativeTerritorialEntity, Dolní Bukovsko]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Dolní Bukovsko
Triple: [České Budějovice District, containsAdministrativeTerritorialEntity, Dolní Bukovsko]
Generated description
Dolní Bukovsko is a market town in the South Bohemian Region of the Czech Republic known for its rural character and historical architecture.

Provenance (5 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_69f05b0d1e7c819092baab93d3fe277e completed April 28, 2026, 7 a.m.
NER Named-entity recognition batch_69f65eddbca081908efdd224d5f49ae4 completed May 2, 2026, 8:30 p.m.
NED1 Entity disambiguation (via context triple) batch_6a24ec5af1448190b7439a6936b80fc8 completed June 7, 2026, 3:58 a.m.
NEDg Description generation batch_6a24f066b990819095925ff855a3370e completed June 7, 2026, 4:15 a.m.
NED2 Entity disambiguation (via description) batch_6a24f4d232f08190808f832d0536033c completed June 7, 2026, 4:34 a.m.
Created at: April 28, 2026, 9:06 a.m.