Triple
T3827049
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lara Beach |
E88714
|
entity |
| Predicate | hasNearbyCity |
P350
|
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: [Lara Beach, hasNearbyCity, Antalya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Antalya Context triple: [Lara Beach, hasNearbyCity, 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.
Izmir
Izmir is a major Turkish coastal city known as an important commercial and cultural hub on the Aegean Sea.
-
C.
Samsun
Samsun is a major Turkish port city on the Black Sea coast, known as an important regional hub for maritime trade and industry.
-
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.
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.
- 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_69aed9538cf881909d9ce8ca4ac7c18c |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeeb64c72c8190b5f3d376aa4ee933 |
completed | March 9, 2026, 3:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b56b3607948190972f83023ba04f55 |
completed | March 14, 2026, 2:05 p.m. |
Created at: March 9, 2026, 3:17 p.m.