Triple

T26401082
Position Surface form Disambiguated ID Type / Status
Subject Lviny Most E663697 entity
Predicate hasNearbyStreet P8235 FINISHED
Object Malaya Podyacheskaya Street
Malaya Podyacheskaya Street is a historic street in central Saint Petersburg, Russia, known for its 18th–19th century architecture and proximity to the city’s canals and landmarks.
 E1819302 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: Malaya Podyacheskaya Street | Statement: [Lviny Most, hasNearbyStreet, Malaya Podyacheskaya Street]
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: Malaya Podyacheskaya Street
Triple: [Lviny Most, hasNearbyStreet, Malaya Podyacheskaya Street]
Generated description
Malaya Podyacheskaya Street is a historic street in central Saint Petersburg, Russia, known for its 18th–19th century architecture and proximity to the city’s canals and landmarks.

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_69ee883823988190b418b111be28a44a completed April 26, 2026, 9:48 p.m.
NER Named-entity recognition batch_69f610f493188190aea2bf6268995310 completed May 2, 2026, 2:57 p.m.
NED1 Entity disambiguation (via context triple) batch_6a164154ce1881908206b07bfcbfe378 completed May 27, 2026, 12:56 a.m.
NEDg Description generation batch_6a1642a04a9c81908f196894b8f4bdf5 completed May 27, 2026, 1:02 a.m.
NED2 Entity disambiguation (via description) batch_6a164322f1148190b37794a5fc54f184 completed May 27, 2026, 1:04 a.m.
Created at: April 26, 2026, 11:32 p.m.