Triple

T26004892
Position Surface form Disambiguated ID Type / Status
Subject Saint Petersburg street network E646730 entity
Predicate hasComponent P35 FINISHED
Object Obukhovskoy Oborony Prospekt
Obukhovskoy Oborony Prospekt is a major avenue in Saint Petersburg, Russia, known for connecting industrial districts and residential areas in the city’s southeast.
 E1781173 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: Obukhovskoy Oborony Prospekt | Statement: [Saint Petersburg street network, hasComponent, Obukhovskoy Oborony Prospekt]
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: Obukhovskoy Oborony Prospekt
Triple: [Saint Petersburg street network, hasComponent, Obukhovskoy Oborony Prospekt]
Generated description
Obukhovskoy Oborony Prospekt is a major avenue in Saint Petersburg, Russia, known for connecting industrial districts and residential areas in the city’s southeast.

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_69e77e89d5848190b54352cdb74f6029 completed April 21, 2026, 1:41 p.m.
NER Named-entity recognition batch_69f6057964948190b3ecab50a47a5e7c completed May 2, 2026, 2:08 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12d0a74e0c81908696df5d652653c4 completed May 24, 2026, 10:19 a.m.
NEDg Description generation batch_6a12d18a985c819080daa18aa946feaa completed May 24, 2026, 10:23 a.m.
NED2 Entity disambiguation (via description) batch_6a12d290bc5081909bd6c027b8a5b4d4 completed May 24, 2026, 10:27 a.m.
Created at: April 22, 2026, 9 a.m.