Triple

T34901316
Position Surface form Disambiguated ID Type / Status
Subject Anzersky Island E1006594 entity
Predicate isPartOfAdministrativeUnit P63290 FINISHED
Object Primorsky District
Primorsky District is an administrative district in Arkhangelsk Oblast, Russia, located along the White Sea coast and encompassing numerous islands and rural localities.
E2290201 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: Primorsky District | Statement: [Anzersky Island, isPartOfAdministrativeUnit, Primorsky District]
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: Primorsky District
Triple: [Anzersky Island, isPartOfAdministrativeUnit, Primorsky District]
Generated description
Primorsky District is an administrative district in Arkhangelsk Oblast, Russia, located along the White Sea coast and encompassing numerous islands and rural localities.

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_69f76dbfe5788190ad8b64f241f470c8 completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f781e86c008190a15e54b2efd0ef84 completed May 3, 2026, 5:12 p.m.
NED1 Entity disambiguation (via context triple) batch_6a5ba99528cc8190b925b39eee4cafb9 completed July 18, 2026, 4:28 p.m.
NEDg Description generation batch_6a5ba9eba27c8190915fd607dba00b30 completed July 18, 2026, 4:29 p.m.
NED2 Entity disambiguation (via description) batch_6a5baa45da988190be9629dbfe45a3a4 completed July 18, 2026, 4:31 p.m.
Created at: May 3, 2026, 4 p.m.