Triple
T17162896
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bielany |
E416525
|
entity |
| Predicate | hasMetroStation |
P522
|
FINISHED |
| Object | Stare Bielany |
—
|
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: Stare Bielany | Statement: [Bielany, hasMetroStation, Stare Bielany]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stare Bielany Context triple: [Bielany, hasMetroStation, Stare 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.
Wawer
Wawer is a district in southeastern Warsaw, Poland, historically known as the site of a notorious World War II Nazi massacre of Polish civilians.
-
C.
Bodzentyn
Bodzentyn is a small historic town in south-central Poland, known for its medieval ruins and scenic location in the Świętokrzyskie region.
-
D.
Brzesko
Brzesko is a town in southern Poland known for its historical architecture and regional brewing traditions.
-
E.
Mielno
Mielno is a Polish seaside resort town on the Baltic coast, known for its beaches, lakeside setting, and popular summer tourism.
- 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_69d886d279c081909f8ff1f743ddeb69 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3f91316108190b0d856d6fa5cd509 |
completed | April 18, 2026, 9:35 p.m. |
Created at: April 10, 2026, 5:37 a.m.