Triple

T30573293
Position Surface form Disambiguated ID Type / Status
Subject Prospekt Vernadskogo E778174 entity
Predicate hasTransportConnection P845 FINISHED
Object Yugo-Zapadnaya Metro Station
Yugo-Zapadnaya Metro Station is a Moscow Metro station on the Sokolnicheskaya Line that serves as a major transport hub in the southwestern part of the city.
E1921887 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: Yugo-Zapadnaya Metro Station | Statement: [Prospekt Vernadskogo, hasTransportConnection, Yugo-Zapadnaya Metro Station]
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: Yugo-Zapadnaya Metro Station
Triple: [Prospekt Vernadskogo, hasTransportConnection, Yugo-Zapadnaya Metro Station]
Generated description
Yugo-Zapadnaya Metro Station is a Moscow Metro station on the Sokolnicheskaya Line that serves as a major transport hub in the southwestern part of the city.

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_69f2249f8c148190ae7eb3912cde112a completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f6891536e08190afce308c5196b020 completed May 2, 2026, 11:30 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2857036f44819082df03435c3e7366 completed June 9, 2026, 6:10 p.m.
NEDg Description generation batch_6a285afd6ca4819089171143044e0edd completed June 9, 2026, 6:27 p.m.
NED2 Entity disambiguation (via description) batch_6a285b7e582c8190ae005dbc31587e8d completed June 9, 2026, 6:29 p.m.
Created at: April 29, 2026, 8:22 p.m.