Triple
T4184076
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kursunlu Waterfall |
E88267
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Antalya |
E14330
|
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: Antalya | Statement: [Kursunlu Waterfall, locatedNear, Antalya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Antalya Context triple: [Kursunlu Waterfall, locatedNear, Antalya]
-
A.
Antalya
chosen
Antalya is a major resort city on Turkey’s Mediterranean coast, known for its beaches, historic old town, and role as a gateway to the Turkish Riviera.
-
B.
Alanya
Alanya is a popular Mediterranean resort city in southern Turkey, known for its beaches, historic castle, and role as a major tourism hub.
-
C.
Marmaris
Marmaris is a popular Turkish coastal resort city known for its beaches, marinas, and vibrant nightlife along the Mediterranean.
-
D.
Bodrum
Bodrum is a popular coastal city and resort town in southwestern Turkey, known for its beaches, nightlife, and historic landmarks such as Bodrum Castle.
-
E.
Izmir
Izmir is a major Turkish coastal city known as an important commercial and cultural hub on the Aegean Sea.
- 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_69aed9477e8c81908bcb862d2db55b1d |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af0321eee88190871c1d4bf44a5007 |
completed | March 9, 2026, 5:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bda4073aa88190ba64691b93aab900 |
completed | March 20, 2026, 7:46 p.m. |
Created at: March 9, 2026, 3:45 p.m.