Triple
T15293246
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kaposvár railway station |
E365582
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Kaposvár |
E217864
|
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: Kaposvár | Statement: [Kaposvár railway station, locatedIn, Kaposvár]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kaposvár Context triple: [Kaposvár railway station, locatedIn, Kaposvár]
-
A.
Kaposvár
chosen
Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
-
B.
Szekesfehervar
Szekesfehérvár is a historic city in central Hungary that served as a medieval royal seat and coronation site for Hungarian kings.
-
C.
Kalocsa
Kalocsa is a historic town in southern Hungary known as an important Roman Catholic archiepiscopal center and for its traditional paprika production and folk art.
-
D.
Győr
Győr is a historic city in northwestern Hungary, known as an important regional cultural and economic center at the confluence of the Danube, Rába, and Rábca rivers.
-
E.
Veszprém
Veszprém is a historic city in western Hungary known for its medieval castle district and role as a regional cultural and administrative center.
- 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_69d85a103d9081908c1ea6c4c73ac8e3 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03682ea488190ac82fdbd0e855d34 |
completed | April 16, 2026, 1:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00456b590c8190949fd23cb5cec1e8 |
completed | May 10, 2026, 8:44 a.m. |
Created at: April 10, 2026, 3:15 a.m.