Triple
T12754433
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lučenec |
E304819
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Eger |
E338315
|
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: Eger | Statement: [Lučenec, hasTwinTown, Eger]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eger Context triple: [Lučenec, hasTwinTown, Eger]
-
A.
Eger
chosen
Eger is a historic city in northern Hungary known for its baroque architecture, castle, and wine culture.
-
B.
Eger
Eger is the former German name for the Czech town of Cheb, a historic settlement near the German border in western Bohemia.
-
C.
Egerszalók
Egerszalók is a Hungarian village famous for its thermal springs and striking terraced salt hill spa complex.
-
D.
Oroszvár
Oroszvár is a historic locality in present-day western Slovakia (now part of Rusovce, a borough of Bratislava) known in part as a former residence of Princess Louise of Belgium.
-
E.
Komárom
Komárom is a Hungarian town on the Danube River known for its historic fortifications and its twin-city relationship with Komárno in Slovakia.
- 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_69d7bdf1fcd081909ffb0e0d6fa3a07d |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96d89ea70819098c470344f172167 |
completed | April 10, 2026, 9:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f67c9aa6308190bfcb1511a561c0f9 |
completed | May 2, 2026, 10:37 p.m. |
Created at: April 9, 2026, 5:27 p.m.