Triple
T4342519
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roth |
E97815
|
entity |
| Predicate | twinTown |
P1072
|
FINISHED |
| Object | Opava |
E194100
|
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: Opava | Statement: [Roth, twinTown, Opava]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Opava Context triple: [Roth, twinTown, Opava]
-
A.
Opava
chosen
Opava is a historic city in the Czech Republic’s Silesian region, known as a former political and cultural center of Silesia.
-
B.
Plzeň
Plzeň is a major city in western Bohemia in the Czech Republic, known for its brewing tradition and industrial heritage.
-
C.
Ostrava
Ostrava is a major industrial and cultural city in the northeastern Czech Republic, near the borders with Poland and Slovakia.
-
D.
Liberec
Liberec is a city in the northern Czech Republic known for its textile industry heritage, mountainous surroundings, and the landmark Ještěd Tower.
-
E.
Olomouc
Olomouc is a historic city in the eastern Czech Republic known for its well-preserved old town, Baroque architecture, and UNESCO-listed Holy Trinity Column.
- 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_69b34548402c819085ab68b27c235a87 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b351878c148190b384053479d41caf |
completed | March 12, 2026, 11:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b66b29cbe4819083788fe6e3e8ba3b |
completed | March 15, 2026, 8:17 a.m. |
Created at: March 12, 2026, 11:14 p.m.