Triple
T11623675
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bursa Province |
E276205
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Yenişehir |
E395064
|
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: Yenişehir | Statement: [Bursa Province, containsCity, Yenişehir]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yenişehir Context triple: [Bursa Province, containsCity, Yenişehir]
-
A.
Yenişehir
chosen
Yenişehir is a district and town in Bursa Province in northwestern Turkey, known for its agricultural production and regional airport serving the Bursa area.
-
B.
Çerkezköy
Çerkezköy is an industrial and residential town in northwestern Turkey, located in Tekirdağ Province within the Thrace region.
-
C.
Büyükerşen
Büyükerşen is a Turkish surname most prominently associated with Yılmaz Büyükerşen, a well-known academic and long-serving mayor of Eskişehir.
-
D.
Şirinköy
Şirinköy is a village located on Gökçeada, Turkey’s largest Aegean island in the Çanakkale Province.
-
E.
Yenipazar
Yenipazar is a small town and district in northwestern Turkey known for its rural character and traditional Anatolian lifestyle.
- 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_69d6aafa51148190ab84940694c00235 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a122a3708190ab6513dad4c4fde7 |
completed | April 10, 2026, 7:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f019075f4c81908e0cde830231b229 |
completed | April 28, 2026, 2:18 a.m. |
Created at: April 8, 2026, 9:39 p.m.