Triple
T9703660
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dunaújváros |
E234840
|
entity |
| Predicate | hasFormerName |
P65
|
FINISHED |
| Object | Dunapentele |
E815472
|
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: Dunapentele | Statement: [Dunaújváros, hasFormerName, Dunapentele]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dunapentele Context triple: [Dunaújváros, hasFormerName, Dunapentele]
-
A.
Bodrog
Bodrog is a river in Central Europe that flows through Slovakia and Hungary before joining the Tisza River.
-
B.
Dunaharaszti
Dunaharaszti is a town in central Hungary that functions largely as a suburban residential and industrial area near Budapest.
-
C.
Ségny
Ségny is a small commune in eastern France’s Ain department, situated near the Swiss border in the Auvergne-Rhône-Alpes region.
-
D.
Nagyerdő
Nagyerdő is a large, historic forested park and recreational area in Debrecen, Hungary, known for its natural beauty and cultural attractions.
-
E.
Pentele
chosen
Pentele is the historical settlement that later developed into the modern Hungarian industrial city of Dunaújváros.
- 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_69ca84cc78808190a56f3402b7c139a7 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9d73a0148190ad4178fd462cdd9c |
completed | April 1, 2026, 10:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1d59e0c8c8190888b56d75f9ba2c2 |
completed | April 5, 2026, 3:23 a.m. |
Created at: March 30, 2026, 8:18 p.m.