Triple

T38276911
Position Surface form Disambiguated ID Type / Status
Subject Prospekt Prosveshcheniya E1021985 entity
Predicate hasEntrance P6140 FINISHED
Object Prospekt Prosveshcheniya avenue
Prospekt Prosveshcheniya avenue is a major thoroughfare in Saint Petersburg, Russia, known for its residential districts, commercial areas, and access to the Prospekt Prosveshcheniya metro station.
E2289049 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: Prospekt Prosveshcheniya avenue | Statement: [Prospekt Prosveshcheniya, hasEntrance, Prospekt Prosveshcheniya avenue]
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: Prospekt Prosveshcheniya avenue
Triple: [Prospekt Prosveshcheniya, hasEntrance, Prospekt Prosveshcheniya avenue]
Generated description
Prospekt Prosveshcheniya avenue is a major thoroughfare in Saint Petersburg, Russia, known for its residential districts, commercial areas, and access to the Prospekt Prosveshcheniya metro station.

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_69f76df0cddc81908d16c1556ff4097f completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69fcc59178ec81908346d0ba5601df83 completed May 7, 2026, 5:02 p.m.
NED1 Entity disambiguation (via context triple) batch_6a5afde5811c8190ba829d913cb1a6f2 completed July 18, 2026, 4:15 a.m.
NEDg Description generation batch_6a5afef6b6088190849ef8a13be7d01d completed July 18, 2026, 4:20 a.m.
NED2 Entity disambiguation (via description) batch_6a5aff3c16d48190884d86bca02d4bf9 completed July 18, 2026, 4:21 a.m.
Created at: May 3, 2026, 4:30 p.m.