Triple
T17493797
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Serik |
E425998
|
entity |
| Predicate | nearbyAttraction |
P3449
|
FINISHED |
| Object | Belek |
—
|
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: Belek | Statement: [Serik, nearbyAttraction, Belek]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Belek Context triple: [Serik, nearbyAttraction, 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.
Seydikemer
Seydikemer is a rural district and town in southwestern Turkey known for its agricultural landscape and proximity to popular coastal and historical sites in Muğla Province.
-
D.
Güzelyurt
Güzelyurt is a town in the northwestern part of Cyprus, known for its citrus orchards and archaeological sites.
-
E.
Güzelyurt
Güzelyurt is a historic town in Turkey’s Cappadocia region, known for its rock-cut churches, underground cities, and scenic valleys.
- 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_69d889dccf7481909264a1844a2e9100 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e451d782688190afb76fa080867315 |
completed | April 19, 2026, 3:53 a.m. |
Created at: April 10, 2026, 5:48 a.m.