Triple
T6922637
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kumamoto, Japan |
E160223
|
entity |
| Predicate | twinnedWith |
P1072
|
FINISHED |
| Object | Heidelberg, Germany |
E15415
|
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: Heidelberg, Germany | Statement: [Kumamoto, Japan, twinnedWith, Heidelberg, Germany]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Heidelberg, Germany Context triple: [Kumamoto, Japan, twinnedWith, Heidelberg, Germany]
-
A.
Tübingen, Germany
Tübingen, Germany, is a historic university town in the state of Baden-Württemberg known for its medieval old town and renowned Eberhard Karls University.
-
B.
Heidelberg
chosen
Heidelberg is a historic university city in southwestern Germany renowned for its picturesque old town, castle ruins, and one of Europe’s oldest universities.
-
C.
Heidelberg
Heidelberg is a suburb of Melbourne, Australia, known for its historic role in Australian Impressionism and its location along the Yarra River.
-
D.
Heidelberg
Heidelberg is a South African town known for its historical significance and role as a regional service and commercial center.
-
E.
Giessen, Germany
Giessen, Germany is a central German university town in the state of Hesse, known for its large student population and academic institutions.
- 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_69c6884d350081908d8a970e4d40ad78 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6d9fd159c819092a69d1a24e22dd5 |
completed | March 27, 2026, 7:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c75137dd848190b35ff72725f886ba |
completed | March 28, 2026, 3:55 a.m. |
Created at: March 27, 2026, 2:26 p.m.