Triple
T998288
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Caucasus |
E21544
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Eurasia |
E9404
|
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: Eurasia | Statement: [South Caucasus, partOf, Eurasia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eurasia Context triple: [South Caucasus, partOf, Eurasia]
-
A.
Eurasia
chosen
Eurasia is the vast combined continental landmass of Europe and Asia, forming the largest continuous land area on Earth.
-
B.
Europa
Europa is a figure in Greek mythology, a Phoenician princess famously abducted by Zeus and later the eponymous queen of Crete.
-
C.
Europa
Europa is one of Jupiter’s large icy moons, notable for its smooth frozen surface and the subsurface ocean that makes it a prime candidate in the search for extraterrestrial life.
-
D.
North Asia
North Asia is the vast, sparsely populated northern part of the Asian continent, dominated by Siberia and characterized by its cold climate and extensive forests and tundra.
-
E.
Europe
Europe is a diverse continent in the Northern Hemisphere known for its rich history, cultural heritage, and significant influence on global politics, economics, and science.
- 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_69a493c476b48190b41fc5e793171cc6 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b4e2ad9c81908a0f488d3f261fc3 |
completed | March 1, 2026, 9:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac6f0503ec81908112cf30ca85530a |
completed | March 7, 2026, 6:31 p.m. |
Created at: March 1, 2026, 7:41 p.m.