Triple
T710851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hungary–Austria border |
E14202
|
entity |
| Predicate | adjacentToCity |
P5707
|
FINISHED |
| Object | Kőszeg |
E99815
|
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: Kőszeg | Statement: [Hungary–Austria border, adjacentToCity, Kőszeg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kőszeg Context triple: [Hungary–Austria border, adjacentToCity, Kőszeg]
-
A.
Miskolc
Miskolc is a large industrial and cultural city in northeastern Hungary, known for its steel industry, historic center, and nearby cave baths.
-
B.
Sopron
chosen
Sopron is a historic city in western Hungary near the Austrian border, known for its well-preserved medieval old town and wine-making traditions.
-
C.
Szeged
Szeged is a prominent city in southern Hungary known for its university, paprika production, and distinctive Art Nouveau architecture.
-
D.
Szentendre
Szentendre is a picturesque riverside town near Budapest in Hungary, known for its baroque architecture, art galleries, and vibrant cultural scene.
-
E.
Debrecen
Debrecen is Hungary’s second-largest city and a key cultural, economic, and educational center in the country’s eastern 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_69a4934a36e081909e7abef98b898a4e |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a55c99fc8190941c5fd18551792a |
completed | March 1, 2026, 8:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7a3a8bcc8819091c785ad953ddc54 |
completed | March 4, 2026, 3:14 a.m. |
Created at: March 1, 2026, 7:36 p.m.