Triple

T21368904
Position Surface form Disambiguated ID Type / Status
Subject E40 motorway E526996 entity
Predicate passesThroughCity P416 FINISHED
Object Aktobe NE NERFINISHED

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: Aktobe | Statement: [E40 motorway, passesThroughCity, Aktobe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Aktobe
Context triple: [E40 motorway, passesThroughCity, Aktobe]
  • A. Aktobe chosen
    Aktobe is a large industrial and cultural center in western Kazakhstan, known for its role in the country’s oil, gas, and mining sectors.
  • B. Aktobe Region
    Aktobe Region is a large administrative region in western Kazakhstan known for its significant mineral resources and strategic location bordering Russia and several other Kazakh regions.
  • C. Kunak
    Kunak is a small coastal town and administrative center in Sabah, Malaysia, known for its agriculture and proximity to rich marine and rainforest areas.
  • D. Akmolinsk
    Akmolinsk is the former name of Kazakhstan’s capital city, now known as Astana.
  • E. Ust-Kamenogorsk
    Ust-Kamenogorsk is an industrial city in northeastern Kazakhstan, known as a major center for metallurgy and winter sports.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b51e80808190ba5cb05667af02a9 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69ee5baf5fb4819093f8d8afdd83ffdb completed April 26, 2026, 6:38 p.m.
Created at: April 16, 2026, 5:09 p.m.