Triple
T4057026
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Turkish Riviera |
E84718
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object |
Belek
Belek is a popular resort town on Turkey’s Mediterranean coast, known for its beaches, luxury hotels, and championship golf courses.
|
E419535
|
NE FINISHED |
How this triple was built (4 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: [Turkish Riviera, hasMajorCity, Belek]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Belek Context triple: [Turkish Riviera, hasMajorCity, Belek]
-
A.
Kanık
Kanık is the surname of the influential Turkish poet Orhan Veli Kanık, a leading figure in modern Turkish literature and the Garip movement.
-
B.
Karaköy
Karaköy is a historic waterfront neighborhood in Istanbul known for its bustling port, cafes, and mix of traditional and modern urban life.
-
C.
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.
-
D.
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.
-
E.
Kemer
Kemer is a popular seaside resort town on Turkey’s Mediterranean coast, known for its beaches, marinas, and proximity to the Taurus Mountains.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Belek Triple: [Turkish Riviera, hasMajorCity, Belek]
Generated description
Belek is a popular resort town on Turkey’s Mediterranean coast, known for its beaches, luxury hotels, and championship golf courses.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Belek Target entity description: Belek is a popular resort town on Turkey’s Mediterranean coast, known for its beaches, luxury hotels, and championship golf courses.
-
A.
Kanık
Kanık is the surname of the influential Turkish poet Orhan Veli Kanık, a leading figure in modern Turkish literature and the Garip movement.
-
B.
Karaköy
Karaköy is a historic waterfront neighborhood in Istanbul known for its bustling port, cafes, and mix of traditional and modern urban life.
-
C.
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.
-
D.
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.
-
E.
Kemer
Kemer is a popular seaside resort town on Turkey’s Mediterranean coast, known for its beaches, marinas, and proximity to the Taurus Mountains.
- F. None of above. chosen
Provenance (5 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_69b589d11b0881909984a0b4f738b444 |
completed | March 14, 2026, 4:16 p.m. |
| NEDg | Description generation | batch_69b58abc256c8190ad37c8d213b8f11e |
completed | March 14, 2026, 4:20 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b58b41916c81909eb2e5ab482a5fe1 |
completed | March 14, 2026, 4:22 p.m. |
Created at: March 9, 2026, 3:38 p.m.