Triple
T802437
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wrocław |
E17157
|
entity |
| Predicate | formerName |
P65
|
FINISHED |
| Object | Breslau |
E17157
|
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: Breslau | Statement: [Wrocław, formerName, Breslau]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Breslau Context triple: [Wrocław, formerName, Breslau]
-
A.
Dresden
Dresden is a historic cultural and economic center in eastern Germany, renowned for its baroque architecture, art collections, and reconstruction after World War II.
-
B.
Görlitz
Görlitz is a historic city in eastern Germany on the Lusatian Neisse River, known for its well-preserved old town and role as a popular film location.
-
C.
Cieszyn Silesia
Cieszyn Silesia is a historical and ethnically diverse borderland region centered around the city of Cieszyn, spanning areas of present-day Poland and the Czech Republic.
-
D.
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.
-
E.
Chemnitz
Chemnitz is a city in eastern Germany known for its industrial heritage and post-reunification urban redevelopment.
- 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_69a49378b9c48190adbf5f62e5b7aca1 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4aabd9fc081908ccadd8e8769de2d |
completed | March 1, 2026, 9:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac6efcc37c81908fd49ad65818eb0c |
completed | March 7, 2026, 6:31 p.m. |
Created at: March 1, 2026, 7:38 p.m.