Triple
T6651732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nysa Kłodzka |
E150837
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Brzeg |
E412025
|
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: Brzeg | Statement: [Nysa Kłodzka, flowsThrough, Brzeg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brzeg Context triple: [Nysa Kłodzka, flowsThrough, Brzeg]
-
A.
Brzeg
chosen
Brzeg is a historic town in southwestern Poland known for its Renaissance castle and well-preserved old town.
-
B.
Brzesko
Brzesko is a town in southern Poland known for its historical architecture and regional brewing traditions.
-
C.
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.
-
D.
Ustka
Ustka is a Baltic Sea coastal town in northern Poland known as a popular seaside resort and fishing port.
-
E.
Olsztynek
Olsztynek is a small historic town in northern Poland known for its open-air ethnographic museum and location within the picturesque Warmian-Masurian lake district.
- 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_69c687f2c9508190a60b9aad31d3f358 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6b0458fb48190a76d8d1d6273a92b |
completed | March 27, 2026, 4:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d5087b394c8190baa1ef5dbc92a0c8 |
completed | April 7, 2026, 1:36 p.m. |
Created at: March 27, 2026, 2:01 p.m.