Triple
T3595152
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eupatoria |
E76119
|
entity |
| Predicate | historicalName |
P65
|
FINISHED |
| Object | Kezlev |
E76119
|
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: Kezlev | Statement: [Eupatoria, historicalName, Kezlev]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kezlev Context triple: [Eupatoria, historicalName, Kezlev]
-
A.
Kezlev
chosen
Kezlev is the historical Crimean Tatar name for the city now known as Eupatoria, a coastal town on the western shore of Crimea.
-
B.
Vytegra
Vytegra is a small town in northwestern Russia known as a regional center near Lake Onega and the White Sea–Baltic Canal.
-
C.
Kelmis
Kelmis is a municipality in eastern Belgium located in the country's German-speaking region, known for its historical zinc mining industry and borderland character near Germany and the Netherlands.
-
D.
Kimry
Kimry is a small Russian town on the Volga River known historically for its shoemaking industry and wooden architecture.
-
E.
Krakhuna
Krakhuna is a Georgian white grape variety from the Imereti region, known for producing aromatic, full-bodied wines with pronounced acidity.
- 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_69ad85d8042081908af94a04c410dec0 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc15f41cc819085b3e897d823757d |
completed | March 8, 2026, 6:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4030fab188190b8de1a4b4d625f00 |
completed | March 13, 2026, 12:29 p.m. |
Created at: March 8, 2026, 3:22 p.m.