Triple
T5020593
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montauban |
E112838
|
entity |
| Predicate | twinTown |
P1072
|
FINISHED |
| Object | Kladno |
E181630
|
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: Kladno | Statement: [Montauban, twinTown, Kladno]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kladno Context triple: [Montauban, twinTown, Kladno]
-
A.
Kladno
chosen
Kladno is an industrial city in the Czech Republic known historically for coal mining and steel production.
-
B.
Velenje
Velenje is a modern industrial town in northern Slovenia known for its coal mining heritage, large lakeside recreational area, and one of the largest Tito statues in the world.
-
C.
Sevnica
Sevnica is a small town in central Slovenia known as the childhood home of former U.S. First Lady Melania Trump.
-
D.
Radeče
Radeče is a small town in central Slovenia, situated on the banks of the Sava River and known for its paper industry and scenic surroundings.
-
E.
Lučenec
Lučenec is a town in southern Slovakia known as a regional center of trade, transport, and culture in the Novohrad area.
- 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_69bd4435c2f48190be593158cbfcf8a3 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd736399ac8190aa38efc4b4edc6a2 |
completed | March 20, 2026, 4:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be927f4ad0819096826f6cb141c90b |
completed | March 21, 2026, 12:43 p.m. |
Created at: March 20, 2026, 1:36 p.m.