Triple
T14865787
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Angyalföld |
E349611
|
entity |
| Predicate | adjacentTo |
P224
|
FINISHED |
| Object | Zugló |
E349621
|
NE FINISHED |
How this triple was built (2 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: Zugló | Statement: [Angyalföld, adjacentTo, Zugló]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zugló Context triple: [Angyalföld, adjacentTo, Zugló]
-
A.
Zugló
chosen
Zugló is Budapest’s 14th district, a largely residential area known for its parks, historic villas, and major landmarks such as City Park and Heroes’ Square.
-
B.
Zala
Zala is a river in western Hungary that flows into Lake Balaton and lends its name to the surrounding Zala region.
-
C.
Trencsén
Trencsén is a historic town in present-day Slovakia, known for its medieval castle and its role as an important regional center in the former Upper Hungary.
-
D.
Bochsa
Bochsa is the surname of Nicolas-Charles Bochsa, a 19th-century French composer, harpist, and influential music teacher.
-
E.
Oberá
Oberá is a major inland city in northeastern Argentina known for its cultural diversity and role as an agricultural and commercial center in Misiones Province.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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. |
Created at: April 10, 2026, 1:55 a.m.