Triple

T14084862
Position Surface form Disambiguated ID Type / Status
Subject BC Samara E338964 entity
Predicate homeVenueLocation P19090 FINISHED
Object Samara, Russia E67593 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: Samara, Russia | Statement: [BC Samara, homeVenueLocation, Samara, Russia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Samara, Russia
Context triple: [BC Samara, homeVenueLocation, Samara, Russia]
  • A. Samara
    Samara is a design-focused housing and urban innovation company co-founded by Airbnb’s Joe Gebbia to explore new forms of living and community.
  • B. Samara
    Samara is a city in northwestern Nigeria that forms part of the urban area of Zaria in Kaduna State.
  • C. Samara chosen
    Samara is a major Russian city on the Volga River known as an important industrial, cultural, and transportation hub.
  • D. Lesosibirsk
    Lesosibirsk is a town in central Siberia, Russia, known historically as a major timber-processing and river port center on the Yenisei River.
  • E. Krasnoyarsk, Russia
    Krasnoyarsk is a major industrial and cultural city in central Siberia, Russia, situated on the Yenisei River and known as a key hub of the region.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d81c687b0c819087fd9ed4198403f8 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de5edff1b881909ea56dc2429ef2dd completed April 14, 2026, 3:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd6d756bb48190ae5598e48b281f71 completed May 8, 2026, 4:58 a.m.
Created at: April 9, 2026, 10:21 p.m.