Triple
T9949517
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Csongrád-Csanád County |
E195292
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Mórahalom
Mórahalom is a small town in southern Hungary near the Serbian border, known for its thermal spa and role as a local agricultural and tourism center.
|
E833189
|
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: Mórahalom | Statement: [Csongrád-Csanád County, contains, Mórahalom]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mórahalom Context triple: [Csongrád-Csanád County, contains, Mórahalom]
-
A.
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.
-
B.
Mátészalka
Mátészalka is a town in northeastern Hungary known as a local administrative and economic center within the Northern Great Plain region.
-
C.
Kiskunhalas
Kiskunhalas is a town in southern Hungary known for its traditional lace-making and agricultural economy.
-
D.
Bicske
Bicske is a small town in central Hungary known for its historical significance and location along major transportation routes west of Budapest.
-
E.
Oroszlány
Oroszlány is a town in northwestern Hungary known historically for its coal mining and industrial character.
- 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: Mórahalom Triple: [Csongrád-Csanád County, contains, Mórahalom]
Generated description
Mórahalom is a small town in southern Hungary near the Serbian border, known for its thermal spa and role as a local agricultural and tourism center.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mórahalom Target entity description: Mórahalom is a small town in southern Hungary near the Serbian border, known for its thermal spa and role as a local agricultural and tourism center.
-
A.
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.
-
B.
Mátészalka
Mátészalka is a town in northeastern Hungary known as a local administrative and economic center within the Northern Great Plain region.
-
C.
Kiskunhalas
Kiskunhalas is a town in southern Hungary known for its traditional lace-making and agricultural economy.
-
D.
Bicske
Bicske is a small town in central Hungary known for its historical significance and location along major transportation routes west of Budapest.
-
E.
Oroszlány
Oroszlány is a town in northwestern Hungary known historically for its coal mining and industrial character.
- 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_69ca82e96a108190932bd1fc4acd73a0 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb65a4e6c8190968192a24aad1b7d |
completed | April 2, 2026, 12:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d257a164308190b88432b914ea7f1a |
completed | April 5, 2026, 12:37 p.m. |
| NEDg | Description generation | batch_69d25924eb2481909dbf135c387051e3 |
completed | April 5, 2026, 12:44 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d259b292c88190818f512f90a641f3 |
completed | April 5, 2026, 12:46 p.m. |
Created at: March 30, 2026, 8:45 p.m.