Triple
T4057098
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Antalya Airport |
E84719
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Belek |
E419535
|
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: Belek | Statement: [Antalya Airport, near, Belek]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Belek Context triple: [Antalya Airport, near, Belek]
-
A.
Belek
chosen
Belek is a popular resort town on Turkey’s Mediterranean coast, known for its beaches, luxury hotels, and championship golf courses.
-
B.
Kanık
Kanık is the surname of the influential Turkish poet Orhan Veli Kanık, a leading figure in modern Turkish literature and the Garip movement.
-
C.
Karaköy
Karaköy is a historic waterfront neighborhood in Istanbul known for its bustling port, cafes, and mix of traditional and modern urban life.
-
D.
Gölbaşı
Gölbaşı is a district and suburban area of Ankara in central Turkey, known for its lakes, recreational areas, and proximity to the capital city.
-
E.
Nallıhan
Nallıhan is a district and town in Turkey known for its natural landscapes, including colorful rock formations and rich birdlife, located within Ankara Province.
- 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_69aed933bec881909edfa28ebb69c634 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefbad953c81909d7b42fae5db9f25 |
completed | March 9, 2026, 4:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b595ffd8b4819099bb5698d9cc10e0 |
completed | March 14, 2026, 5:08 p.m. |
Created at: March 9, 2026, 3:38 p.m.