Triple
T18713478
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Istres |
E457575
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Tiszaújváros |
—
|
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: Tiszaújváros | Statement: [Istres, hasTwinTown, Tiszaújváros]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tiszaújváros Context triple: [Istres, hasTwinTown, Tiszaújváros]
-
A.
Tiszaújváros
chosen
Tiszaújváros is an industrial town in northeastern Hungary known for its large chemical and energy industries and its location along the Tisza River.
-
B.
Tiszavasvári
Tiszavasvári is a town in northeastern Hungary known for its agricultural surroundings and location within the Northern Great Plain region.
-
C.
Vasvár
Vasvár is a small historic town in western Hungary known for its medieval heritage and role as a former county seat.
-
D.
Dunakeszi
Dunakeszi is a town in Hungary located just north of Budapest, known as a rapidly growing suburban and commuter settlement along the Danube in Pest County.
-
E.
Dombóvár
Dombóvár is a town in southern Hungary known as an important local transport and economic center within Tolna County.
- 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_69d8d392aad081909fe31aa03e6e97d1 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e56ab352b481909b444e7c476898f4 |
completed | April 19, 2026, 11:52 p.m. |
Created at: April 10, 2026, 11:50 a.m.