Triple
T3503017
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Simferopol–Yalta highway |
E74011
|
entity |
| Predicate | terminusA |
P388
|
FINISHED |
| Object | Simferopol |
E10809
|
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: Simferopol | Statement: [Simferopol–Yalta highway, terminusA, Simferopol]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Simferopol Context triple: [Simferopol–Yalta highway, terminusA, Simferopol]
-
A.
Simferopol
chosen
Simferopol is the administrative and cultural center of Crimea, known as a key regional hub for transportation, education, and industry.
-
B.
Yevpatoria
Yevpatoria is a historic resort and port city on the western coast of Crimea, known for its beaches, therapeutic mud treatments, and diverse cultural heritage.
-
C.
Kherson
Kherson is a port city in southern Ukraine near the Black Sea, historically significant as a shipbuilding and industrial center and strategically important due to its location on the Dnieper River.
-
D.
Mykolaiv
Mykolaiv is a major shipbuilding and industrial city in southern Ukraine located near the Black Sea.
-
E.
Donetsk
Donetsk is a major industrial city in eastern Ukraine, historically known for its coal mining and steel production.
- 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_69ad85ce7a9c81909ddc5cf0cb67a6e3 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbbf0c2b48190b49923137bb9e45d |
completed | March 8, 2026, 6:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5335e1e348190912455cd90009558 |
completed | March 14, 2026, 10:07 a.m. |
Created at: March 8, 2026, 3:18 p.m.