Triple

T25406325
Position Surface form Disambiguated ID Type / Status
Subject Rybatskoye E636566 entity
Predicate fareZone P844 FINISHED
Object Saint Petersburg Metro unified zone
Saint Petersburg Metro unified zone is the single integrated fare area of the Saint Petersburg Metro system, allowing travel across all its stations under a unified ticketing scheme.
E1680531 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: Saint Petersburg Metro unified zone | Statement: [Rybatskoye, fareZone, Saint Petersburg Metro unified zone]
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: Saint Petersburg Metro unified zone
Triple: [Rybatskoye, fareZone, Saint Petersburg Metro unified zone]
Generated description
Saint Petersburg Metro unified zone is the single integrated fare area of the Saint Petersburg Metro system, allowing travel across all its stations under a unified ticketing scheme.

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_69e75db361d881908d8701c856da6413 completed April 21, 2026, 11:21 a.m.
NER Named-entity recognition batch_69f5850091e88190a6fbb9b2c33f46ed completed May 2, 2026, 5 a.m.
NED1 Entity disambiguation (via context triple) batch_6a10898db5048190843ed8918a76697d completed May 22, 2026, 4:51 p.m.
NEDg Description generation batch_6a108b6308e4819085c42bf69f2c0f70 completed May 22, 2026, 4:59 p.m.
NED2 Entity disambiguation (via description) batch_6a108bc789948190bca50782a54091f8 completed May 22, 2026, 5 p.m.
Created at: April 21, 2026, 1:52 p.m.