Triple
T14932554
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Litoměřice |
E372303
|
entity |
| Predicate | twinTown |
P1072
|
FINISHED |
| Object | Düren |
E466244
|
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: Düren | Statement: [Litoměřice, twinTown, Düren]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Düren Context triple: [Litoměřice, twinTown, Düren]
-
A.
Düren
chosen
Düren is a town in western Germany’s North Rhine-Westphalia, known as an industrial center situated between Cologne and Aachen.
-
B.
Darıca
Darıca is a coastal town and district in northwestern Turkey, situated on the Sea of Marmara and known for its zoo, recreation areas, and proximity to Istanbul.
-
C.
Derince
Derince is an industrial and port city located on the Sea of Marmara in northwestern Turkey.
-
D.
Odunpazarı
Odunpazarı is a historic central district of Eskişehir in northwestern Turkey, known for its traditional Ottoman houses and cultural heritage.
-
E.
Büyükerşen
Büyükerşen is a Turkish surname most prominently associated with Yılmaz Büyükerşen, a well-known academic and long-serving mayor of Eskişehir.
- 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_69d85cc9da0c81908d583ca3f63a3908 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded646a0808190ba5c0c91bde011c5 |
completed | April 15, 2026, 12:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe72c86b5c81909e601a3fc78276bd |
completed | May 8, 2026, 11:33 p.m. |
Created at: April 10, 2026, 2:37 a.m.