Triple
T20093780
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Janusz Laskowski |
E496342
|
entity |
| Predicate | workLocation |
P7
|
FINISHED |
| Object | Wrocław |
—
|
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: Wrocław | Statement: [Janusz Laskowski, workLocation, Wrocław]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wrocław Context triple: [Janusz Laskowski, workLocation, Wrocław]
-
A.
Wrocław
chosen
Wrocław is a major historic city in southwestern Poland, known for its picturesque Old Town, numerous bridges over the Oder River, and role as a cultural and academic center.
-
B.
Katowice
Katowice is a major industrial and cultural city in southern Poland, known as the capital of the Silesian region.
-
C.
Poznań
Poznań is a historic and economically significant city in western Poland, known for its medieval Old Town, role as an early center of Polish statehood, and status as a major academic and industrial hub.
-
D.
Kraków
Kraków is one of Poland’s oldest and most historically significant cities, renowned for its well-preserved medieval core, royal heritage, and cultural institutions.
-
E.
Wolsztyn
Wolsztyn is a town in western Poland known for its historic steam locomotive depot and annual steam engine parade.
- 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_69da626eee3881909f3454986d4a6511 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6666a7a94819080ebabfba9762f97 |
completed | April 20, 2026, 5:46 p.m. |
Created at: April 11, 2026, 11:23 p.m.