Triple
T17642930
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Turquoise Coast |
E429282
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object | Kalkan |
—
|
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: Kalkan | Statement: [Turquoise Coast, hasTown, Kalkan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kalkan Context triple: [Turquoise Coast, hasTown, Kalkan]
-
A.
Kalkan
chosen
Kalkan is a picturesque seaside town on Turkey’s Mediterranean coast, known for its historic architecture, steep cobbled streets, and upscale tourism.
-
B.
Kalkaji
Kalkaji is a prominent residential and commercial neighborhood in South Delhi, known for the famous Kalkaji Temple and its proximity to major business and shopping hubs.
-
C.
Kalsa
Kalsa is a historic district of Palermo, Italy, known for its Arab-Norman heritage, medieval streets, and vibrant cultural life.
-
D.
Kandan
Kandan is a locality within Beijing’s Fengtai District, known primarily as a residential and urban neighborhood area.
-
E.
Kalyar
Kalyar is a notable Sufi pilgrimage town in India associated with the Chishti Order and revered for its prominent dargah (shrine).
- 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_69d889e2c2608190b762e76d9b2262f1 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46de7055c819080d315bb3637882b |
completed | April 19, 2026, 5:53 a.m. |
Created at: April 10, 2026, 6:03 a.m.