Triple
T14310181
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kőbánya-Kispest |
E354805
|
entity |
| Predicate | nameContains |
P5298
|
FINISHED |
| Object | Kispest |
E1168597
|
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: Kispest | Statement: [Kőbánya-Kispest, nameContains, Kispest]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kispest Context triple: [Kőbánya-Kispest, nameContains, Kispest]
-
A.
Kispest
chosen
Kispest is a district in Budapest, Hungary, known as a largely residential area with its own local commercial centers and transport connections.
-
B.
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.
-
C.
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.
-
D.
Kaposvár
Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
-
E.
Zalaegerszeg
Zalaegerszeg is a city in western Hungary that serves as the administrative center of Zala County and a regional economic and cultural hub.
- 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_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_69ff677935408190a28af4cd34d82aa4 |
completed | May 9, 2026, 4:57 p.m. |
Created at: April 10, 2026, 1:12 a.m.