Triple
T14865910
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | District IV of Budapest |
E349614
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Székesdűlő
Székesdűlő is a neighborhood within Budapest’s 4th District (Újpest), primarily known as a residential and industrial suburban area.
|
E1190172
|
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: Székesdűlő | Statement: [District IV of Budapest, hasPart, Székesdűlő]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Székesdűlő Context triple: [District IV of Budapest, hasPart, Székesdűlő]
-
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.
Szekszárd
Szekszárd is a historic Hungarian town renowned as one of the country’s leading red wine regions and the administrative center of Tolna County.
-
C.
Diósgyőr
Diósgyőr is a historic district of Miskolc in northeastern Hungary, best known for its medieval castle and surrounding cultural heritage.
-
D.
Szécsény
Szécsény is a historic town in northern Hungary known for its medieval architecture and role as a regional center in Nógrád County.
-
E.
Dombóvár
Dombóvár is a town in southern Hungary known as an important local transport and economic center within Tolna County.
- 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: Székesdűlő Triple: [District IV of Budapest, hasPart, Székesdűlő]
Generated description
Székesdűlő is a neighborhood within Budapest’s 4th District (Újpest), primarily known as a residential and industrial suburban area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Székesdűlő Target entity description: Székesdűlő is a neighborhood within Budapest’s 4th District (Újpest), primarily known as a residential and industrial suburban area.
-
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.
Szekszárd
Szekszárd is a historic Hungarian town renowned as one of the country’s leading red wine regions and the administrative center of Tolna County.
-
C.
Diósgyőr
Diósgyőr is a historic district of Miskolc in northeastern Hungary, best known for its medieval castle and surrounding cultural heritage.
-
D.
Szécsény
Szécsény is a historic town in northern Hungary known for its medieval architecture and role as a regional center in Nógrád County.
-
E.
Dombóvár
Dombóvár is a town in southern Hungary known as an important local transport and economic center within Tolna County.
- 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_69d822ed7e1881909b90fca143ad7e34 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded5761c688190b4477cb081554b51 |
completed | April 15, 2026, 12:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffcf0743288190b3bec8c48b5c7893 |
completed | May 10, 2026, 12:19 a.m. |
| NEDg | Description generation | batch_69ffd10730408190b84b4a4b4f1d3c4b |
completed | May 10, 2026, 12:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffd1662aa88190886f47a85387b1cf |
completed | May 10, 2026, 12:29 a.m. |
Created at: April 10, 2026, 1:55 a.m.