Triple
T14310160
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kőbánya-Kispest |
E354805
|
entity |
| Predicate | locatedInDistrict |
P40
|
FINISHED |
| Object |
Kispest
Kispest is a district in Budapest, Hungary, known as a largely residential area with its own local commercial centers and transport connections.
|
E1168597
|
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: Kispest | Statement: [Kőbánya-Kispest, locatedInDistrict, Kispest]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kispest Context triple: [Kőbánya-Kispest, locatedInDistrict, Kispest]
-
A.
Budaörs
Budaörs is a suburban town near Budapest in Hungary, known for its rapid post-communist development and role as a commercial and residential hub.
-
B.
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.
-
C.
Kaposvár
Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
-
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.
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: Kispest Triple: [Kőbánya-Kispest, locatedInDistrict, Kispest]
Generated description
Kispest is a district in Budapest, Hungary, known as a largely residential area with its own local commercial centers and transport connections.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kispest Target entity description: Kispest is a district in Budapest, Hungary, known as a largely residential area with its own local commercial centers and transport connections.
-
A.
Budaörs
Budaörs is a suburban town near Budapest in Hungary, known for its rapid post-communist development and role as a commercial and residential hub.
-
B.
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.
-
C.
Kaposvár
Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
-
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.
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_69d8278ed42c8190b9f882dcce611347 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de85b26da48190a96e2f60ace51335 |
completed | April 14, 2026, 6:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff5f1f80648190a4a0e8260ac95194 |
completed | May 9, 2026, 4:21 p.m. |
| NEDg | Description generation | batch_69ff5fea7cb48190a1acb9201a12fa32 |
completed | May 9, 2026, 4:25 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff62e84bec81908a4885bf7f8f3749 |
completed | May 9, 2026, 4:38 p.m. |
Created at: April 10, 2026, 1:12 a.m.