Triple

T12128851
Position Surface form Disambiguated ID Type / Status
Subject Wawrzyszew metro station E288879 entity
Predicate locatedInDistrict P40 FINISHED
Object Bielany E416525 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: Bielany | Statement: [Wawrzyszew metro station, locatedInDistrict, Bielany]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bielany
Context triple: [Wawrzyszew metro station, locatedInDistrict, Bielany]
  • A. Bielany chosen
    Bielany is a northern district of Warsaw, Poland, known for its residential neighborhoods, green spaces, and connection to the city center via the Warsaw Metro.
  • B. Brzesko
    Brzesko is a town in southern Poland known for its historical architecture and regional brewing traditions.
  • C. Parczew
    Parczew is a small town in eastern Poland known for its historical roots and location within the Lublin region.
  • D. Wołosate
    Wołosate is a small village in southeastern Poland’s Bieszczady Mountains, known as a remote hiking base and the terminus of the Main Beskid Trail.
  • E. Otwock
    Otwock is a town in east-central Poland known as a suburban spa and residential area within the broader Warsaw 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_69d6ab4b5e4c81909950b17151eb0951 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d9158a2c2c8190aaff9d0cce177565 completed April 10, 2026, 3:21 p.m.
NED1 Entity disambiguation (via context triple) batch_6a009d20a42c819090319629544fa349 completed May 10, 2026, 2:58 p.m.
Created at: April 8, 2026, 9:49 p.m.