Triple
T18540809
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Konya Plain |
E453092
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Konya city |
—
|
NE NERFINISHED |
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: Konya city | Statement: [Konya Plain, near, Konya city]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Konya city Context triple: [Konya Plain, near, Konya city]
-
A.
Konya
chosen
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.
-
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.
Kütahya
Kütahya is a historic city in western Turkey known for its Ottoman-era architecture and traditional ceramic and tile production.
-
D.
Eskişehir
Eskişehir is a major university and industrial city in northwestern Turkey, known for its vibrant student life, modern urban design, and rich cultural heritage.
-
E.
Karabük
Karabük is an industrial city in northern Turkey best known for its historic iron and steel industry and its proximity to the UNESCO-listed Ottoman town of Safranbolu.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d387b5548190aa030dad2cb4947e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e534b64178819082861a03fd067095 |
completed | April 19, 2026, 8:01 p.m. |
Created at: April 10, 2026, 11:37 a.m.