Triple
T5402408
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Komárom-Esztergom County |
E120808
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Oroszlány
Oroszlány is a town in northwestern Hungary known historically for its coal mining and industrial character.
|
E524952
|
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: Oroszlány | Statement: [Komárom-Esztergom County, contains, Oroszlány]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oroszlány Context triple: [Komárom-Esztergom County, contains, Oroszlány]
-
A.
Tiszaújváros
Tiszaújváros is an industrial town in northeastern Hungary known for its large chemical and energy industries and its location along the Tisza River.
-
B.
Mátraháza
Mátraháza is a small mountain resort village in northern Hungary, known for its scenic location in the Mátra range and its hiking and wellness tourism.
-
C.
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.
-
D.
Tihany
Tihany is a historic village on the northern shore of Lake Balaton in Hungary, renowned for its Benedictine abbey, scenic peninsula, and traditional architecture.
-
E.
Csákvár
Csákvár is a small town in central Hungary known for its rural character and location within the Transdanubian region.
- 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: Oroszlány Triple: [Komárom-Esztergom County, contains, Oroszlány]
Generated description
Oroszlány is a town in northwestern Hungary known historically for its coal mining and industrial character.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Oroszlány Target entity description: Oroszlány is a town in northwestern Hungary known historically for its coal mining and industrial character.
-
A.
Tiszaújváros
Tiszaújváros is an industrial town in northeastern Hungary known for its large chemical and energy industries and its location along the Tisza River.
-
B.
Mátraháza
Mátraháza is a small mountain resort village in northern Hungary, known for its scenic location in the Mátra range and its hiking and wellness tourism.
-
C.
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.
-
D.
Tihany
Tihany is a historic village on the northern shore of Lake Balaton in Hungary, renowned for its Benedictine abbey, scenic peninsula, and traditional architecture.
-
E.
Csákvár
Csákvár is a small town in central Hungary known for its rural character and location within the Transdanubian region.
- 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_69bd46391c0c81909fa484446732b6a3 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd87731c1c81909a4dc865282bd289 |
completed | March 20, 2026, 5:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf8335f5a48190973622011df4c108 |
completed | March 22, 2026, 5:50 a.m. |
| NEDg | Description generation | batch_69bf83c14dac8190878c6929618a7905 |
completed | March 22, 2026, 5:53 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69bf8437ed0c8190a286489b0a34586a |
completed | March 22, 2026, 5:55 a.m. |
Created at: March 20, 2026, 2:04 p.m.