Triple
T5491911
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Asian Turkey |
E123721
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Kayseri |
E152975
|
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: Kayseri | Statement: [Asian Turkey, containsCity, Kayseri]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kayseri Context triple: [Asian Turkey, containsCity, Kayseri]
-
A.
Kayseri
chosen
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.
-
B.
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.
-
C.
Samsun
Samsun is a major Turkish port city on the Black Sea coast, known as an important regional hub for maritime trade and industry.
-
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.
Gaziantep
Gaziantep is a major city in southeastern Turkey known for its rich history, cultural heritage, and renowned pistachio-based cuisine, especially baklava.
- 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_69bd464a2d908190869324ce176779c8 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd9280403c8190baaa3f7923449a37 |
completed | March 20, 2026, 6:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c02777c0908190a0aebbbca3b7b0a6 |
completed | March 22, 2026, 5:31 p.m. |
Created at: March 20, 2026, 2:10 p.m.