Triple
T20983665
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tata |
E516832
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Tatabánya |
—
|
NE NERFINISHED |
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: Tatabánya | Statement: [Tata, near, Tatabánya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tatabánya Context triple: [Tata, near, Tatabánya]
-
A.
Tatabánya
chosen
Tatabánya is an industrial city in northwestern Hungary known for its mining heritage and role as a regional economic center.
-
B.
Budakeszi
Budakeszi is a small town in Hungary, located just west of Budapest and known for its surrounding forests and natural recreational areas.
-
C.
Bátonyterenye
Bátonyterenye is a small industrial town in northern Hungary known for its mining heritage and location in Nógrád County.
-
D.
Törökbálint
Törökbálint is a town in Pest County, Hungary, located just southwest of Budapest and known as a suburban residential area with growing commercial and industrial zones.
-
E.
Tiszaújváros
Tiszaújváros is an industrial town in northeastern Hungary known for its large chemical and energy industries and its location along the Tisza River.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4ffac148190bbade9f0eceb660b |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6fbe1474c8190912b90da0973f99f |
completed | April 21, 2026, 4:24 a.m. |
Created at: April 16, 2026, 1:48 p.m.