Triple

T20365324
Position Surface form Disambiguated ID Type / Status
Subject Chaunsky District E496891 entity
Predicate contains P35 FINISHED
Object Chaun River
The Chaun River is a river in the Chukotka Autonomous Okrug of northeastern Russia that flows into the East Siberian Sea.
E2284307 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: Chaun River | Statement: [Chaunsky District, contains, Chaun River]
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: Chaun River
Triple: [Chaunsky District, contains, Chaun River]
Generated description
The Chaun River is a river in the Chukotka Autonomous Okrug of northeastern Russia that flows into the East Siberian Sea.

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_69e0b4a4f9b081908a5a021919c21ccb completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e67870f4448190ab63cbe03542de21 completed April 20, 2026, 7:03 p.m.
NED1 Entity disambiguation (via context triple) batch_6a4343c3ae7c8190bde7e5149d18ad0a completed June 30, 2026, 4:19 a.m.
NEDg Description generation batch_6a43444b89ec8190a4500e2b261544d3 completed June 30, 2026, 4:21 a.m.
NED2 Entity disambiguation (via description) batch_6a4344ac41bc8190b872afac148c0f62 completed June 30, 2026, 4:23 a.m.
Created at: April 16, 2026, 11:26 a.m.