Triple

T33967305
Position Surface form Disambiguated ID Type / Status
Subject Jan Bedřich Kittl E870888 entity
Predicate studiedUnder P7251 FINISHED
Object Václav Jan Tomášek
Václav Jan Tomášek was a prominent Czech composer, pianist, and influential music teacher of the early 19th century, known for his piano works and for mentoring many significant Bohemian musicians.
E2243861 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: Václav Jan Tomášek | Statement: [Jan Bedřich Kittl, studiedUnder, Václav Jan Tomášek]
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: Václav Jan Tomášek
Triple: [Jan Bedřich Kittl, studiedUnder, Václav Jan Tomášek]
Generated description
Václav Jan Tomášek was a prominent Czech composer, pianist, and influential music teacher of the early 19th century, known for his piano works and for mentoring many significant Bohemian musicians.

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_69f3499ce8e88190b66e1d49ad8c7037 completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f70323e7cc8190b881428ec90f774b completed May 3, 2026, 8:11 a.m.
NED1 Entity disambiguation (via context triple) batch_6a40f15e67c881909176789bd30dd41b completed June 28, 2026, 10:03 a.m.
NEDg Description generation batch_6a40f23ecbc081909576b02690c2cc92 completed June 28, 2026, 10:06 a.m.
NED2 Entity disambiguation (via description) batch_6a40f2defa148190a773cdebe781a243 completed June 28, 2026, 10:09 a.m.
Created at: May 1, 2026, 1:50 a.m.