Triple
T15774768
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haná |
E382461
|
entity |
| Predicate | majorCity |
P316
|
FINISHED |
| Object | Přerov |
E584545
|
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: Přerov | Statement: [Haná, majorCity, Přerov]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Přerov Context triple: [Haná, majorCity, Přerov]
-
A.
Přerov
chosen
Přerov is a city in the Olomouc Region of the Czech Republic, known as an important industrial and transport hub on the Bečva River.
-
B.
Vsetín
Vsetín is a town in the eastern Czech Republic known as an industrial and cultural center of the Moravian Wallachia region.
-
C.
Břeclav
Břeclav is a town in the South Moravian Region of the Czech Republic, known as a local transport hub and gateway to the Lednice–Valtice cultural landscape near the Austrian and Slovak borders.
-
D.
Bražec
Bražec is a small village and administrative part of the town of Náchod in the Hradec Králové Region of the Czech Republic.
-
E.
Říčany
Říčany is a town in the Czech Republic, located just southeast of Prague and known as a popular residential and commuter suburb with historical roots.
- 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_69d86da09a10819082fe9797b23e4664 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e05198c1588190a65e23c18443eb5c |
completed | April 16, 2026, 3:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffe469012481908fd9955c72e756a3 |
completed | May 10, 2026, 1:50 a.m. |
Created at: April 10, 2026, 4:47 a.m.