Triple
T14864884
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tabán |
E349590
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object |
Víziváros
Víziváros is a historic riverside neighborhood in Buda, Budapest, known for its medieval origins, proximity to the Danube, and views of the Castle District.
|
E1123491
|
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: Víziváros | Statement: [Tabán, locatedNear, Víziváros]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Víziváros Context triple: [Tabán, locatedNear, Víziváros]
-
A.
Donau City
Donau City is a modern business and residential district in Vienna known for its high-rise buildings and proximity to the Danube River.
-
B.
Schwanenstadt
Schwanenstadt is a small Austrian town in the state of Upper Austria, known as the birthplace of composer Franz Xaver Süssmayr.
-
C.
Water City
Water City is the popular nickname of Liaocheng, a Chinese city renowned for its extensive waterways and historic lakeside scenery.
-
D.
Leninváros
Leninváros was the former name of the Hungarian industrial town now known as Tiszaújváros, developed during the socialist era.
-
E.
Nova Venécia
Nova Venécia is a municipality in the northern region of the Brazilian state of Espírito Santo, known for its agricultural economy and growing regional commerce.
- 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: Víziváros Triple: [Tabán, locatedNear, Víziváros]
Generated description
Víziváros is a historic riverside neighborhood in Buda, Budapest, known for its medieval origins, proximity to the Danube, and views of the Castle District.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Víziváros Target entity description: Víziváros is a historic riverside neighborhood in Buda, Budapest, known for its medieval origins, proximity to the Danube, and views of the Castle District.
-
A.
Donau City
Donau City is a modern business and residential district in Vienna known for its high-rise buildings and proximity to the Danube River.
-
B.
Schwanenstadt
Schwanenstadt is a small Austrian town in the state of Upper Austria, known as the birthplace of composer Franz Xaver Süssmayr.
-
C.
Water City
Water City is the popular nickname of Liaocheng, a Chinese city renowned for its extensive waterways and historic lakeside scenery.
-
D.
Leninváros
Leninváros was the former name of the Hungarian industrial town now known as Tiszaújváros, developed during the socialist era.
-
E.
Nova Venécia
Nova Venécia is a municipality in the northern region of the Brazilian state of Espírito Santo, known for its agricultural economy and growing regional commerce.
- 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_69fe650e8aec8190acd4a9cb9cad2039 |
completed | May 8, 2026, 10:34 p.m. |
| NEDg | Description generation | batch_69fe65ac6a5c81908621dc17edc6b04f |
completed | May 8, 2026, 10:37 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe6697fe3881908aae42abe56d86f8 |
completed | May 8, 2026, 10:41 p.m. |
Created at: April 10, 2026, 1:54 a.m.