Triple

T28425253
Position Surface form Disambiguated ID Type / Status
Subject Kokořín Castle E720055 entity
Predicate builtFor P1261 FINISHED
Object Hynek Berka of Dubá
Hynek Berka of Dubá was a 14th-century Bohemian nobleman and member of the influential Berka of Dubá family, prominent in the medieval history of the Kingdom of Bohemia.
E1816689 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: Hynek Berka of Dubá | Statement: [Kokořín Castle, builtFor, Hynek Berka of Dubá]
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: Hynek Berka of Dubá
Triple: [Kokořín Castle, builtFor, Hynek Berka of Dubá]
Generated description
Hynek Berka of Dubá was a 14th-century Bohemian nobleman and member of the influential Berka of Dubá family, prominent in the medieval history of the Kingdom of Bohemia.

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_69eff6f1c5088190bc24bfbf92f9c017 completed April 27, 2026, 11:53 p.m.
NER Named-entity recognition batch_69f64dfced2881909b10e62108c89f60 completed May 2, 2026, 7:18 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1633224f8481908f8d3b098c4dc8aa completed May 26, 2026, 11:56 p.m.
NEDg Description generation batch_6a1634256d74819090c95b262c5e6873 completed May 27, 2026, midnight
NED2 Entity disambiguation (via description) batch_6a1635bc8c488190bb284e3caa3c6641 completed May 27, 2026, 12:07 a.m.
Created at: April 28, 2026, 1:36 a.m.