Triple
T12690794
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Altay S.K. |
E303197
|
entity |
| Predicate | basedIn |
P40
|
FINISHED |
| Object | İzmir |
E10416
|
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: İzmir | Statement: [Altay S.K., basedIn, İzmir]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: İzmir Context triple: [Altay S.K., basedIn, İzmir]
-
A.
Izmir
chosen
Izmir is a major Turkish coastal city known as an important commercial and cultural hub on the Aegean Sea.
-
B.
İzmit
İzmit is a city in northwestern Turkey on the Gulf of İzmit, historically significant as the site of ancient Nicomedia and an important industrial and transportation hub near Istanbul.
-
C.
Denizli
Denizli is a major industrial and commercial city in western Turkey, known for its textile production and proximity to the famous Pamukkale travertine terraces.
-
D.
Nazilli
Nazilli is a town and district in Turkey’s Aydın Province, known for its agricultural production and location in the fertile Büyük Menderes River valley.
-
E.
Samsun
Samsun is a major Turkish port city on the Black Sea coast, known as an important regional hub for maritime trade and industry.
- 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_69d7bdef90d48190b46b88270e780946 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961dabb38819087738361f9de8066 |
completed | April 10, 2026, 8:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f671acbd5c8190ad8d1d2f18868369 |
completed | May 2, 2026, 9:50 p.m. |
Created at: April 9, 2026, 5:22 p.m.