Triple
T1913040
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miskolc |
E38152
|
entity |
| Predicate | hasHistoricalCenter |
P17588
|
FINISHED |
| Object | Downtown Miskolc |
E38152
|
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: Downtown Miskolc | Statement: [Miskolc, hasHistoricalCenter, Downtown Miskolc]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Downtown Miskolc Context triple: [Miskolc, hasHistoricalCenter, Downtown Miskolc]
-
A.
Miskolc
chosen
Miskolc is a large industrial and cultural city in northeastern Hungary, known for its steel industry, historic center, and nearby cave baths.
-
B.
Komló
Komló is a town in southern Hungary known historically for its coal mining and hop-growing industries.
-
C.
Kaposvár, Hungary
Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
-
D.
Szentendre
Szentendre is a picturesque riverside town near Budapest in Hungary, known for its baroque architecture, art galleries, and vibrant cultural scene.
-
E.
Budapest XVI. district
Budapest XVI. district is a residential and suburban district on the eastern side of Hungary’s capital, known for its green areas and small-town atmosphere within the city.
- 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_69a8862a26088190aae5243695aeefc0 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb1e26b948190aa194c30755ac5df |
completed | March 7, 2026, 5:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adf3d5972881908856b75b324a1ad2 |
completed | March 8, 2026, 10:10 p.m. |
Created at: March 4, 2026, 7:35 p.m.