Triple
T6169217
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anapa |
E137647
|
entity |
| Predicate | hasNearbyCity |
P350
|
FINISHED |
| Object | Gelendzhik |
E166972
|
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: Gelendzhik | Statement: [Anapa, hasNearbyCity, Gelendzhik]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gelendzhik Context triple: [Anapa, hasNearbyCity, Gelendzhik]
-
A.
Gelendzhik
chosen
Gelendzhik is a Black Sea resort city in southern Russia known for its beaches, scenic bay, and tourism infrastructure.
-
B.
Novorossiysk
Novorossiysk is a major port city on Russia’s Black Sea coast that serves as an important naval and commercial hub.
-
C.
Tosno
Tosno is a town in northwestern Russia that serves as an administrative and transportation hub southeast of Saint Petersburg.
-
D.
Alexeyevsk
Alexeyevsk is the former name of the Russian town now known as Belogorsk, located in Amur Oblast in the Russian Far East.
-
E.
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.
- 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_69c008a68c508190a8d78245c865960e |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05d8de56481909583104c70a52616 |
completed | March 22, 2026, 9:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c16ee938748190ad03e19c241b0881 |
completed | March 23, 2026, 4:48 p.m. |
Created at: March 22, 2026, 4:18 p.m.