Triple
T15316736
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bolhrad Raion |
E366177
|
entity |
| Predicate | hasMajorSettlement |
P316
|
FINISHED |
| Object | Artsyz |
E874063
|
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: Artsyz | Statement: [Bolhrad Raion, hasMajorSettlement, Artsyz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Artsyz Context triple: [Bolhrad Raion, hasMajorSettlement, Artsyz]
-
A.
Artsyz
chosen
Artsyz is a small town in southwestern Ukraine known for its agricultural surroundings and location within the historical region of Bessarabia.
-
B.
Artěl
Artěl was a Czech artists' and designers' cooperative active in the early 20th century that promoted modern applied arts and design.
-
C.
Artis
Artis is a masculine given name most notably borne by Hall of Fame basketball center Artis Gilmore.
-
D.
Artaria
Artaria was a prominent Viennese music publishing house known for issuing works by major Classical-era composers such as Mozart and Haydn.
-
E.
Artés
Artés is a municipality in the comarca of Bages in Catalonia, Spain, known for its wine and cava 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_69d85a121520819093dcce999fdefe1a |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03dd1d384819098f38402a8740d91 |
completed | April 16, 2026, 1:39 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fef8a688a48190848eb7f065aba146 |
completed | May 9, 2026, 9:04 a.m. |
Created at: April 10, 2026, 3:16 a.m.