Triple
T20245311
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Southern Demerdzhi |
E498408
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Alushta |
—
|
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: Alushta | Statement: [Southern Demerdzhi, near, Alushta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alushta Context triple: [Southern Demerdzhi, near, Alushta]
-
A.
Alushta
chosen
Alushta is a resort town on the southern coast of Crimea, known for its beaches, mild climate, and role as a popular Black Sea tourist destination.
-
B.
Liman
Liman is a surname most notably associated with American film director and producer Doug Liman.
-
C.
Vineta
Vineta is a legendary medieval Baltic Sea trading city, often associated with the island of Wolin and famed in myth as a wealthy metropolis lost beneath the waves.
-
D.
Imotski
Imotski is a small historic town in inland Dalmatia, Croatia, known for its karst landscape and the nearby Blue and Red Lakes.
-
E.
Marina di Ragusa
Marina di Ragusa is a popular seaside resort town on Sicily’s southern coast, known for its sandy beaches, modern marina, and vibrant summer nightlife.
- 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_69da6274c58c81909c646eabed6f4f30 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e673a10ab48190a408a5d2c2b0808b |
completed | April 20, 2026, 6:42 p.m. |
Created at: April 11, 2026, 11:40 p.m.