Triple
T622398
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Poznań |
E14540
|
entity |
| Predicate | twinCity |
P1072
|
FINISHED |
| Object | Brno |
E47149
|
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: Brno | Statement: [Poznań, twinCity, Brno]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brno Context triple: [Poznań, twinCity, Brno]
-
A.
Brno
chosen
Brno is the second-largest city in the Czech Republic, known as a major cultural, educational, and industrial center in the historical region of Moravia.
-
B.
Ostrava
Ostrava is a major industrial and cultural city in the northeastern Czech Republic, near the borders with Poland and Slovakia.
-
C.
Plzeň
Plzeň is a major city in western Bohemia in the Czech Republic, known for its brewing tradition and industrial heritage.
-
D.
Hradec Králové
Hradec Králové is a historic city in the Czech Republic known for its educational institutions, modernist architecture, and role as a regional cultural and economic center.
-
E.
Zlín
Zlín is a city in the Czech Republic known for its modernist architecture and historical association with the Baťa shoe company.
- 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_69a4934b17c881909ace8270e8ddd202 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49e402d9c8190936896e3ebb6edc5 |
completed | March 1, 2026, 8:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a68916d9f081909a14612144491fb7 |
completed | March 3, 2026, 7:09 a.m. |
Created at: March 1, 2026, 7:35 p.m.