Triple
T7670846
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Silesia in Katowice |
E173742
|
entity |
| Predicate | hasCityCampus |
P5417
|
FINISHED |
| Object | Sosnowiec |
E426926
|
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: Sosnowiec | Statement: [University of Silesia in Katowice, hasCityCampus, Sosnowiec]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sosnowiec Context triple: [University of Silesia in Katowice, hasCityCampus, Sosnowiec]
-
A.
Sosnowiec
chosen
Sosnowiec is an industrial city in southern Poland, located in the Silesian Voivodeship and known as part of the Upper Silesian metropolitan area.
-
B.
Skierniewice
Skierniewice is a historic city in central Poland known for its horticultural research center and annual Skierniewice Fruit and Vegetable Festival.
-
C.
Chorzów
Chorzów is an industrial city in southern Poland’s Silesian region, known for its heavy industry heritage and the extensive Silesian Park.
-
D.
Polkowice
Polkowice is a town in southwestern Poland known for its copper mining industry and location within the Lower Silesian region.
-
E.
Stalowa Wola
Stalowa Wola is an industrial city in southeastern Poland, historically known as a major center of heavy industry and steel production.
- 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_69c699562484819086752091e3164a27 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c701dd3c808190990e07ced94b3297 |
completed | March 27, 2026, 10:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e42d406e2c8190ad27f3a5276be25f |
completed | April 19, 2026, 1:17 a.m. |
Created at: March 27, 2026, 4 p.m.