Triple
T18852807
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saleph River |
E461086
|
entity |
| Predicate | hasMouthNear |
P350
|
FINISHED |
| Object | Silifke |
—
|
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: Silifke | Statement: [Saleph River, hasMouthNear, Silifke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Silifke Context triple: [Saleph River, hasMouthNear, Silifke]
-
A.
Silifke
chosen
Silifke is a town and district in Mersin Province, southern Turkey, known for its rich ancient history and proximity to important archaeological sites.
-
B.
Zollikofen
Zollikofen is a municipality in the canton of Bern in Switzerland, functioning as a suburban community within the greater Bern metropolitan region.
-
C.
Edenkoben
Edenkoben is a small town in the Palatinate region of Rhineland-Palatinate, Germany, known for its wine production and picturesque location along the German Wine Route.
-
D.
Gilserberg
Gilserberg is a small municipality in the German state of Hesse, known for its rural character and location within the Schwalm-Eder district.
-
E.
Hasliberg
Hasliberg is a Swiss alpine village and municipality in the canton of Bern, known for its mountain scenery and ski and hiking resort facilities.
- 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_69d8dcfa11e4819090ab1ef5bdcd2b2e |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c05a8a708190b9b2b8f95b2f1d23 |
completed | April 20, 2026, 5:57 a.m. |
Created at: April 10, 2026, 11:56 a.m.