Triple
T245024
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hungary |
E5017
|
entity |
| Predicate | majorCity |
P316
|
FINISHED |
| Object |
Debrecen
Debrecen is Hungary’s second-largest city and a key cultural, economic, and educational center in the country’s eastern region.
|
E37144
|
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: Debrecen | Statement: [Hungary, majorCity, Debrecen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Debrecen Context triple: [Hungary, majorCity, Debrecen]
-
A.
Budapest
Budapest is the capital and largest city of Hungary, renowned for its historic architecture, thermal baths, and prominent location along the Danube River.
-
B.
Székesfehérvár
Székesfehérvár is a historic city in central Hungary that served as a medieval royal seat and coronation site for Hungarian kings.
-
C.
Bratislava
Bratislava is the capital and largest city of Slovakia, situated along the Danube River near the borders with Austria and Hungary.
-
D.
Bucharest
Bucharest is the capital and largest city of Romania, known for its mix of historic architecture, wide boulevards, and its role as the country’s political, cultural, and economic center.
-
E.
Lutsk
Lutsk is a historic city in northwestern Ukraine, known as the administrative center of Volyn Oblast and one of the region’s oldest cultural and economic hubs.
- 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: Debrecen Triple: [Hungary, majorCity, Debrecen]
Generated description
Debrecen is Hungary’s second-largest city and a key cultural, economic, and educational center in the country’s eastern region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Debrecen Target entity description: Debrecen is Hungary’s second-largest city and a key cultural, economic, and educational center in the country’s eastern region.
-
A.
Budapest
Budapest is the capital and largest city of Hungary, renowned for its historic architecture, thermal baths, and prominent location along the Danube River.
-
B.
Székesfehérvár
Székesfehérvár is a historic city in central Hungary that served as a medieval royal seat and coronation site for Hungarian kings.
-
C.
Bratislava
Bratislava is the capital and largest city of Slovakia, situated along the Danube River near the borders with Austria and Hungary.
-
D.
Bucharest
Bucharest is the capital and largest city of Romania, known for its mix of historic architecture, wide boulevards, and its role as the country’s political, cultural, and economic center.
-
E.
Lutsk
Lutsk is a historic city in northwestern Ukraine, known as the administrative center of Volyn Oblast and one of the region’s oldest cultural and economic hubs.
- 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_69a257c3d0708190b0871c4269d273e6 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25d10ac248190a98dedabf5358668 |
completed | Feb. 28, 2026, 3:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a39d0313b48190a3e8e1667a051610 |
completed | March 1, 2026, 1:57 a.m. |
| NEDg | Description generation | batch_69a39d732c64819086a6e11df23e60ce |
completed | March 1, 2026, 1:59 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a39de4511c8190bf668bc74ba57231 |
completed | March 1, 2026, 2:01 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.