Triple
T17014055
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rába |
E412771
|
entity |
| Predicate | flowsThroughCity |
P10456
|
FINISHED |
| Object | Szentgotthárd |
—
|
NE NERFINISHED |
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: Szentgotthárd | Statement: [Rába, flowsThroughCity, Szentgotthárd]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Szentgotthárd Context triple: [Rába, flowsThroughCity, Szentgotthárd]
-
A.
Szentgotthárd
chosen
Szentgotthárd is a small town in western Hungary near the Austrian and Slovenian borders, known for its historic Cistercian abbey and role in the 1664 Battle of Saint Gotthard.
-
B.
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.
-
C.
Nagyvázsony
Nagyvázsony is a village in Veszprém County, Hungary, known for its historic Kinizsi Castle and traditional rural character.
-
D.
Zalaegerszeg
Zalaegerszeg is a city in western Hungary that serves as the administrative center of Zala County and a regional economic and cultural hub.
-
E.
Harkány
Harkány is a Hungarian spa town in southern Transdanubia renowned for its medicinal thermal baths and health tourism.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d886cc4170819093deddc7b8b4b6a7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d47e64f081908f43870c7564d0ae |
completed | April 18, 2026, 6:59 p.m. |
Created at: April 10, 2026, 5:33 a.m.