Triple
T6606368
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Upper Silesia |
E149128
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | Zabrze |
E526141
|
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: Zabrze | Statement: [Upper Silesia, hasMajorCity, Zabrze]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zabrze Context triple: [Upper Silesia, hasMajorCity, Zabrze]
-
A.
Zabrze
chosen
Zabrze is an industrial city in the Silesian region of southern Poland, historically known for coal mining and heavy industry.
-
B.
Kalisz
Kalisz is one of Poland’s oldest cities, located in the Greater Poland region and known for its historical architecture and cultural heritage.
-
C.
Zawiercie
Zawiercie is a town in southern Poland’s Silesian Voivodeship, known historically as an industrial and railway hub near the Kraków-Częstochowa Upland.
-
D.
Zgierz
Zgierz is a city in central Poland, historically part of the industrial Łódź region and notable for its textile industry and role in regional trade.
-
E.
Tychy
Tychy is a city in the Silesian region of southern Poland, known for its brewing industry and role as a planned industrial center.
- 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_69c687eaa7508190bb58ce2aa02039b3 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6af143d5c8190b62602602510b1cb |
completed | March 27, 2026, 4:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d23c6f38ac8190a652575b8dc2fd45 |
completed | April 5, 2026, 10:41 a.m. |
Created at: March 27, 2026, 1:57 p.m.