Triple

T2795111
Position Surface form Disambiguated ID Type / Status
Subject Aktobe E53016 entity
Predicate roadConnection P385 FINISHED
Object connected to Oral (Uralsk)
Oral (Uralsk) is a city in western Kazakhstan that serves as an important regional center near the Russian border.
 E299430 NE FINISHED

How this triple was built (4 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: connected to Oral (Uralsk) | Statement: [Aktobe, roadConnection, connected to Oral (Uralsk)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: connected to Oral (Uralsk)
Context triple: [Aktobe, roadConnection, connected to Oral (Uralsk)]
  • A. Boksitogorsk
    Boksitogorsk is a small industrial town in northwestern Russia known for its bauxite mining and alumina production.
  • B. Ulyanov
    Ulyanov is the Russian surname of Vladimir Lenin, the revolutionary leader and founder of the Soviet state.
  • C. Kamyshin
    Kamyshin is a significant industrial and river port city on the Volga River in southwestern Russia.
  • D. Odintsovo
    Odintsovo is a town in western Russia that serves as an important suburban center just outside Moscow.
  • E. Stavropol Upland
    Stavropol Upland is a hilly elevated region in southwestern Russia known for its fertile soils and significant role in the agriculture and landscape of Stavropol Krai.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: connected to Oral (Uralsk)
Triple: [Aktobe, roadConnection, connected to Oral (Uralsk)]
Generated description
Oral (Uralsk) is a city in western Kazakhstan that serves as an important regional center near the Russian border.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: connected to Oral (Uralsk)
Target entity description: Oral (Uralsk) is a city in western Kazakhstan that serves as an important regional center near the Russian border.
  • A. Boksitogorsk
    Boksitogorsk is a small industrial town in northwestern Russia known for its bauxite mining and alumina production.
  • B. Ulyanov
    Ulyanov is the Russian surname of Vladimir Lenin, the revolutionary leader and founder of the Soviet state.
  • C. Kamyshin
    Kamyshin is a significant industrial and river port city on the Volga River in southwestern Russia.
  • D. Odintsovo
    Odintsovo is a town in western Russia that serves as an important suburban center just outside Moscow.
  • E. Stavropol Upland
    Stavropol Upland is a hilly elevated region in southwestern Russia known for its fertile soils and significant role in the agriculture and landscape of Stavropol Krai.
  • F. None of above. chosen

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_69ab495a90788190941b6917e1eca3a6 completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abddd48bcc819083f4ec59d66f0ece completed March 7, 2026, 8:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69afc6618fb88190a653ae0d15e1a0d4 completed March 10, 2026, 7:21 a.m.
NEDg Description generation batch_69afc6d485d88190b281abec460ff24d completed March 10, 2026, 7:23 a.m.
NED2 Entity disambiguation (via description) batch_69afc7527b708190b8adffb18ceb4c82 completed March 10, 2026, 7:25 a.m.
Created at: March 6, 2026, 9:58 p.m.