Triple
T14902301
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Budakeszi Forest |
E360035
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object |
Budakeszi
Budakeszi is a small town in Hungary, located just west of Budapest and known for its surrounding forests and natural recreational areas.
|
E1193342
|
NE FINISHED |
How this triple was built (4 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: Budakeszi | Statement: [Budakeszi Forest, near, Budakeszi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Budakeszi Context triple: [Budakeszi Forest, near, Budakeszi]
-
A.
Dunakeszi
Dunakeszi is a town in Hungary located just north of Budapest, known as a rapidly growing suburban and commuter settlement along the Danube in Pest County.
-
B.
Zalaegerszeg
Zalaegerszeg is a city in western Hungary that serves as the administrative center of Zala County and a regional economic and cultural hub.
-
C.
Dombóvár
Dombóvár is a town in southern Hungary known as an important local transport and economic center within Tolna County.
-
D.
Kispest
Kispest is a district in Budapest, Hungary, known as a largely residential area with its own local commercial centers and transport connections.
-
E.
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.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Budakeszi Triple: [Budakeszi Forest, near, Budakeszi]
Generated description
Budakeszi is a small town in Hungary, located just west of Budapest and known for its surrounding forests and natural recreational areas.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Budakeszi Target entity description: Budakeszi is a small town in Hungary, located just west of Budapest and known for its surrounding forests and natural recreational areas.
-
A.
Dunakeszi
Dunakeszi is a town in Hungary located just north of Budapest, known as a rapidly growing suburban and commuter settlement along the Danube in Pest County.
-
B.
Zalaegerszeg
Zalaegerszeg is a city in western Hungary that serves as the administrative center of Zala County and a regional economic and cultural hub.
-
C.
Dombóvár
Dombóvár is a town in southern Hungary known as an important local transport and economic center within Tolna County.
-
D.
Kispest
Kispest is a district in Budapest, Hungary, known as a largely residential area with its own local commercial centers and transport connections.
-
E.
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.
- F. None of above. chosen
Provenance (5 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_69d827980cbc8190a0c569ae3940a1d9 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69ded60b24008190bd272c0d61329400 |
completed | April 15, 2026, 12:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffe46436048190b79d1d18a179617b |
completed | May 10, 2026, 1:50 a.m. |
| NEDg | Description generation | batch_69ffe5ced2dc8190922b910d1a6c08d3 |
completed | May 10, 2026, 1:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffe687c204819092a4a8de0b9d624d |
completed | May 10, 2026, 1:59 a.m. |
Created at: April 10, 2026, 2:11 a.m.