Triple
T5406838
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Söğüt |
E120912
|
entity |
| Predicate | nearbyCity |
P350
|
FINISHED |
| Object | Eskişehir |
E344696
|
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: Eskişehir | Statement: [Söğüt, nearbyCity, Eskişehir]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eskişehir Context triple: [Söğüt, nearbyCity, Eskişehir]
-
A.
Eskişehir
chosen
Eskişehir is a major university and industrial city in northwestern Turkey, known for its vibrant student life, modern urban design, and rich cultural heritage.
-
B.
Kayseri
Kayseri is a historic city in central Turkey, known for its Seljuk and Ottoman architectural heritage and its role as a major commercial and cultural center in Anatolia.
-
C.
Konya
Konya is a major city in central Anatolia known for its rich Seljuk heritage and as the home of the Sufi mystic Rumi and the Whirling Dervishes.
-
D.
Bursa
Bursa is a major city in northwestern Turkey known historically as the first capital of the Ottoman Empire and today as an important industrial and cultural center.
-
E.
Aksaray
Aksaray is a historic city in central Turkey known for its location on the ancient Silk Road and its proximity to the Cappadocia region.
- 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_69bd46391c0c81909fa484446732b6a3 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd87924c588190beb4a1be27f8d11b |
completed | March 20, 2026, 5:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bff2ad363481908f3f287052233ae8 |
completed | March 22, 2026, 1:46 p.m. |
Created at: March 20, 2026, 2:05 p.m.