Triple
T12418750
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Heilbronn |
E296707
|
entity |
| Predicate | twinTown |
P1072
|
FINISHED |
| Object | Slaný |
E236768
|
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: Slaný | Statement: [Heilbronn, twinTown, Slaný]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Slaný Context triple: [Heilbronn, twinTown, Slaný]
-
A.
Slaný
chosen
Slaný is a historic town in the Czech Republic known for its medieval center and location northwest of Prague.
-
B.
Slaná
Slaná is a river in central Europe that flows through Slovakia and Hungary, where it is known as the Sajó.
-
C.
Svitavy
Svitavy is a town in the Czech Republic best known as the birthplace of Oskar Schindler, the industrialist who saved hundreds of Jews during the Holocaust.
-
D.
Žamberk
Žamberk is a small historic town in the Pardubice Region of the Czech Republic, known for its picturesque setting in the Orlické Foothills and well-preserved architecture.
-
E.
Vsetín
Vsetín is a town in the eastern Czech Republic known as an industrial and cultural center of the Moravian Wallachia region.
- 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_69d6ada0640c81908c061d7fb3d47786 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94d6efd748190a5d9396a343e41e1 |
completed | April 10, 2026, 7:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fba1ad4cc48190a680652efa3ab463 |
completed | May 6, 2026, 8:16 p.m. |
Created at: April 8, 2026, 9:55 p.m.