Triple
T7163597
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zamárdi |
E167008
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Balatonföldvár |
E516630
|
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: Balatonföldvár | Statement: [Zamárdi, locatedNear, Balatonföldvár]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Balatonföldvár Context triple: [Zamárdi, locatedNear, Balatonföldvár]
-
A.
Törökbálint
Törökbálint is a town in Pest County, Hungary, located just southwest of Budapest and known as a suburban residential area with growing commercial and industrial zones.
-
B.
Balatonfűzfő
chosen
Balatonfűzfő is a small Hungarian town on the northern shore of Lake Balaton, known for its lakeside recreation and industrial history.
-
C.
Nagyvázsony
Nagyvázsony is a village in Veszprém County, Hungary, known for its historic Kinizsi Castle and traditional rural character.
-
D.
Várpalota
Várpalota is a town in western Hungary known for its historical castle and industrial heritage.
-
E.
Pécsvárad
Pécsvárad is a small historic town in southern Hungary known for its medieval abbey and scenic setting near the Mecsek Mountains.
- 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_69c68888c10c819095e0383020225758 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e82feee481908fa180ea8c9924fa |
completed | March 27, 2026, 8:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7adc8b06c81909791e38becb594f6 |
completed | March 28, 2026, 10:30 a.m. |
Created at: March 27, 2026, 2:47 p.m.