Triple

T28753347
Position Surface form Disambiguated ID Type / Status
Subject Aptekarsky Island E731594 entity
Predicate hasStreet P959 FINISHED
Object Professora Popova Street
Professora Popova Street is a notable street located on Aptekarsky Island in Saint Petersburg, Russia, known for its proximity to academic and research institutions.
 E1831431 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: Professora Popova Street | Statement: [Aptekarsky Island, hasStreet, Professora Popova 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: Professora Popova Street
Triple: [Aptekarsky Island, hasStreet, Professora Popova Street]
Generated description
Professora Popova Street is a notable street located on Aptekarsky Island in Saint Petersburg, Russia, known for its proximity to academic and research institutions.

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_69f043ed68a881909e858a06bab7a247 completed April 28, 2026, 5:21 a.m.
NER Named-entity recognition batch_69f657f914fc819096cdb3664e962042 completed May 2, 2026, 8 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1ccf74082481908954ff3b0ca572eb completed June 1, 2026, 12:16 a.m.
NEDg Description generation batch_6a1cd02268a88190b51b5602e6916d3e completed June 1, 2026, 12:19 a.m.
NED2 Entity disambiguation (via description) batch_6a24946ccd908190ae144fbc7010aca9 completed June 6, 2026, 9:43 p.m.
Created at: April 28, 2026, 6:08 a.m.