Triple
T12525615
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oral |
E299430
|
entity |
| Predicate | historicalName |
P65
|
FINISHED |
| Object | Uralsk |
E877886
|
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: Uralsk | Statement: [Oral, historicalName, Uralsk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Uralsk Context triple: [Oral, historicalName, Uralsk]
-
A.
Uralsk
chosen
Uralsk is a city in western Kazakhstan located near the Ural River, historically significant as a trading and cultural center at the crossroads of Europe and Asia.
-
B.
Ural
Ural is a Russian automotive brand best known for its heavy-duty off-road trucks and military-grade utility vehicles.
-
C.
Ural
Ural is a Russian professional football club based in Yekaterinburg that competes in the Russian Premier League.
-
D.
Ural region
The Ural region is a historical and geographical area of Russia centered around the Ural Mountains, traditionally seen as a boundary between Europe and Asia and known for its rich mineral resources and industrial centers.
-
E.
Ruß
"Ruß" is a literary work by contemporary German-Turkish author Feridun Zaimoglu, known for its exploration of identity, migration, and marginalized voices in German society.
- 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_69d6ada5cdd48190860d9ce30aff69be |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d9545d7e6c819080c3a85c18caa1ae |
completed | April 10, 2026, 7:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f655762ae88190ab41e23bbd65c566 |
completed | May 2, 2026, 7:50 p.m. |
Created at: April 8, 2026, 9:57 p.m.