Triple
T15877553
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Black Sea drainage basin |
E384987
|
entity |
| Predicate | hasMajorCityOnCoast |
P969
|
FINISHED |
| Object | Novorossiysk |
E31261
|
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: Novorossiysk | Statement: [Black Sea drainage basin, hasMajorCityOnCoast, Novorossiysk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Novorossiysk Context triple: [Black Sea drainage basin, hasMajorCityOnCoast, Novorossiysk]
-
A.
Novorossiysk
chosen
Novorossiysk is a major port city on Russia’s Black Sea coast that serves as an important naval and commercial hub.
-
B.
Gelendzhik
Gelendzhik is a Black Sea resort city in southern Russia known for its beaches, scenic bay, and tourism infrastructure.
-
C.
Volgodonsk
Volgodonsk is an industrial city in southwestern Russia known for its nuclear power plant and location on the Tsimlyansk Reservoir in Rostov Oblast.
-
D.
Zheleznovodsk
Zheleznovodsk is a spa town in Russia’s Stavropol Krai, known for its mineral springs and health resorts in the Caucasus region.
-
E.
Alekseyevskaya
Alekseyevskaya is a Moscow Metro station located on the Kaluzhsko–Rizhskaya Line, serving the Alekseyevsky District in northeastern Moscow.
- 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_69d86da4e86481909f1325fdc971b5ec |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e155fec9d4819081efea504e1e3952 |
completed | April 16, 2026, 9:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffa950a890819092bc1e8895034593 |
completed | May 9, 2026, 9:38 p.m. |
Created at: April 10, 2026, 4:51 a.m.