Triple

T30713083
Position Surface form Disambiguated ID Type / Status
Subject Kvasiny, Czech Republic E781943 entity
Predicate governedBy P46 FINISHED
Object municipal council of Kvasiny
The municipal council of Kvasiny is the local governing body responsible for administering and making decisions for the village of Kvasiny in the Czech Republic.
E1929397 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: municipal council of Kvasiny | Statement: [Kvasiny, Czech Republic, governedBy, municipal council of Kvasiny]
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: municipal council of Kvasiny
Triple: [Kvasiny, Czech Republic, governedBy, municipal council of Kvasiny]
Generated description
The municipal council of Kvasiny is the local governing body responsible for administering and making decisions for the village of Kvasiny in the Czech Republic.

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_69f224acd24481908ed5f96f0d69b5dd completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f68c1fd1e081908fa0a55e82f3030b completed May 2, 2026, 11:43 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2899038f788190bb92d4f78ab0d41e completed June 9, 2026, 10:51 p.m.
NEDg Description generation batch_6a2899f4a9488190a4ef4d96ec795595 completed June 9, 2026, 10:55 p.m.
NED2 Entity disambiguation (via description) batch_6a289ae6104c8190920332fe93d01b15 completed June 9, 2026, 10:59 p.m.
Created at: April 29, 2026, 8:35 p.m.