Triple

T34177403
Position Surface form Disambiguated ID Type / Status
Subject Baikonur E876710 entity
Predicate hasCityDistrict P2709 FINISHED
Object Bostandyk District
Bostandyk District is an administrative district associated with the city of Baikonur in Kazakhstan, known for encompassing part of the area around the Baikonur Cosmodrome.
E2084439 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: Bostandyk District | Statement: [Baikonur, hasCityDistrict, Bostandyk 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: Bostandyk District
Triple: [Baikonur, hasCityDistrict, Bostandyk District]
Generated description
Bostandyk District is an administrative district associated with the city of Baikonur in Kazakhstan, known for encompassing part of the area around the Baikonur Cosmodrome.

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_69f349ae640c8190b9cd220b5368d8b6 completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f7100252d081908f2007bf671eabad completed May 3, 2026, 9:06 a.m.
NED1 Entity disambiguation (via context triple) batch_6a36c1de65648190a5ac61543c335e87 completed June 20, 2026, 4:37 p.m.
NEDg Description generation batch_6a36c3feb66c8190b32f594400bab08b completed June 20, 2026, 4:46 p.m.
NED2 Entity disambiguation (via description) batch_6a36c532b54c8190a87547d1143dd7d2 completed June 20, 2026, 4:52 p.m.
Created at: May 1, 2026, 1:54 a.m.