Triple

T29360841
Position Surface form Disambiguated ID Type / Status
Subject Sosnytsia E744584 entity
Predicate locatedInAdministrativeUnit P40 FINISHED
Object Sosnytsia Raion
Sosnytsia Raion was a former administrative district (raion) in Chernihiv Oblast in northern Ukraine, centered around the urban-type settlement of Sosnytsia.
E1888532 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: Sosnytsia Raion | Statement: [Sosnytsia, locatedInAdministrativeUnit, Sosnytsia Raion]
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: Sosnytsia Raion
Triple: [Sosnytsia, locatedInAdministrativeUnit, Sosnytsia Raion]
Generated description
Sosnytsia Raion was a former administrative district (raion) in Chernihiv Oblast in northern Ukraine, centered around the urban-type settlement of Sosnytsia.

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_69f0a79aee588190b490f19d93c6e52d completed April 28, 2026, 12:27 p.m.
NER Named-entity recognition batch_69f6698892e88190b076bb0cdf159d8b completed May 2, 2026, 9:15 p.m.
NED1 Entity disambiguation (via context triple) batch_6a26f1a68ae881909e6af32d0c9dbde2 completed June 8, 2026, 4:45 p.m.
NEDg Description generation batch_6a26f2a076d4819086a7a4bf85946196 completed June 8, 2026, 4:49 p.m.
NED2 Entity disambiguation (via description) batch_6a26f34f9b488190b90e3e36cf7174dc completed June 8, 2026, 4:52 p.m.
Created at: April 28, 2026, 2:17 p.m.