Triple

T33990406
Position Surface form Disambiguated ID Type / Status
Subject Stone Flower fountain E871525 entity
Predicate designer P184 FINISHED
Object Konstantin Topuridze
Konstantin Topuridze was a Soviet architect and designer best known for creating prominent monumental works, including the iconic Stone Flower fountain at Moscow’s VDNKh.
E2097950 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: Konstantin Topuridze | Statement: [Stone Flower fountain, designer, Konstantin Topuridze]
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: Konstantin Topuridze
Triple: [Stone Flower fountain, designer, Konstantin Topuridze]
Generated description
Konstantin Topuridze was a Soviet architect and designer best known for creating prominent monumental works, including the iconic Stone Flower fountain at Moscow’s VDNKh.

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_69f3499e964c8190b674b03f6f791b4b completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f70391c4c88190b3c5e91d46132c06 completed May 3, 2026, 8:13 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3721151a8c8190a8fe8a1dbc25aaff completed June 20, 2026, 11:24 p.m.
NEDg Description generation batch_6a3721be9f4881908ebee1b76d4ff59f completed June 20, 2026, 11:26 p.m.
NED2 Entity disambiguation (via description) batch_6a37224447088190abded9d7634e4766 completed June 20, 2026, 11:29 p.m.
Created at: May 1, 2026, 1:50 a.m.