Triple
T14865785
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Angyalföld |
E349611
|
entity |
| Predicate | adjacentTo |
P224
|
FINISHED |
| Object |
Újpest
Újpest is a northern district of Budapest, Hungary, known for its residential neighborhoods, industrial heritage, and the Újpest FC football club.
|
E1186913
|
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: Újpest | Statement: [Angyalföld, adjacentTo, Újpest]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Újpest Context triple: [Angyalföld, adjacentTo, Újpest]
-
A.
Újvidék
Újvidék is the Hungarian name for Novi Sad, a major cultural and economic center in northern Serbia and the capital of the autonomous province of Vojvodina.
-
B.
Újbuda
Újbuda is a major residential and commercial district on the Buda side of Budapest, known for its universities, cultural venues, and riverside areas along the Danube.
-
C.
Dunaújváros
Dunaújváros is an industrial city in central Hungary known for its steel production and post-war socialist urban planning.
-
D.
Veszprém
Veszprém is a historic city in western Hungary known for its medieval castle district and role as a regional cultural and administrative center.
-
E.
Sopron
Sopron is a historic city in western Hungary near the Austrian border, known for its well-preserved medieval old town and wine-making traditions.
- 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: Újpest Triple: [Angyalföld, adjacentTo, Újpest]
Generated description
Újpest is a northern district of Budapest, Hungary, known for its residential neighborhoods, industrial heritage, and the Újpest FC football club.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Újpest Target entity description: Újpest is a northern district of Budapest, Hungary, known for its residential neighborhoods, industrial heritage, and the Újpest FC football club.
-
A.
Újvidék
Újvidék is the Hungarian name for Novi Sad, a major cultural and economic center in northern Serbia and the capital of the autonomous province of Vojvodina.
-
B.
Újbuda
Újbuda is a major residential and commercial district on the Buda side of Budapest, known for its universities, cultural venues, and riverside areas along the Danube.
-
C.
Dunaújváros
Dunaújváros is an industrial city in central Hungary known for its steel production and post-war socialist urban planning.
-
D.
Veszprém
Veszprém is a historic city in western Hungary known for its medieval castle district and role as a regional cultural and administrative center.
-
E.
Sopron
Sopron is a historic city in western Hungary near the Austrian border, known for its well-preserved medieval old town and wine-making traditions.
- 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_69ffbe60d4a08190833397c75b56932c |
completed | May 9, 2026, 11:08 p.m. |
| NEDg | Description generation | batch_69ffbf70d6488190944986503882678d |
completed | May 9, 2026, 11:12 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffc08179488190a8f434121bede859 |
completed | May 9, 2026, 11:17 p.m. |
Created at: April 10, 2026, 1:55 a.m.