Triple
T15099964
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sajó River |
E360637
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | 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: Miskolc | Statement: [Sajó River, flowsThrough, Miskolc]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Miskolc Context triple: [Sajó River, flowsThrough, 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.
Kecskemét
Kecskemét is a city in central Hungary known for its Art Nouveau architecture, cultural institutions, and role as an administrative and economic center of the region.
-
C.
Zalaegerszeg
Zalaegerszeg is a city in western Hungary that serves as the administrative center of Zala County and a regional economic and cultural hub.
-
D.
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.
-
E.
Békéscsaba
Békéscsaba is a city in southeastern Hungary known as the administrative center of Békés County and for its cultural and culinary traditions, including its famous sausage.
- 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_69d85a035aa88190b52a139d3a1b7b6d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e00550007481909e02ee1d597a4d37 |
completed | April 15, 2026, 9:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fffee31b70819092d0583100a7101a |
completed | May 10, 2026, 3:43 a.m. |
Created at: April 10, 2026, 3:04 a.m.