Triple
T8043175
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Acrobat and Young Harlequin |
E187482
|
entity |
| Predicate | locatedInCountry |
P40
|
FINISHED |
| Object | Russia |
E10011
|
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: Russia | Statement: [Acrobat and Young Harlequin, locatedInCountry, Russia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Russia Context triple: [Acrobat and Young Harlequin, locatedInCountry, Russia]
-
A.
Russia
chosen
Russia is the world’s largest country by land area, spanning Eastern Europe and northern Asia and exerting major political, military, and cultural influence globally.
-
B.
ROSSIYA
ROSSIYA is the radio callsign used by Rossiya Airlines, a major Russian carrier based in Saint Petersburg.
-
C.
Rusko
Rusko is a small municipality in southwestern Finland known for its rural character and proximity to the city of Turku.
-
D.
Rusa
Rusa is a genus of deer native to South and Southeast Asia, including species such as the Javan rusa and sambar.
-
E.
Russas
Russas is a municipality in the northeastern Brazilian state of Ceará, known for its agricultural activities and semi-arid climate.
- 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_69ca82b00cb48190b59a300f70e97bd7 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3f49dcfc81909ac7c93e19ad05c2 |
completed | March 31, 2026, 3:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc63b72c508190b0ad8acf975c1527 |
completed | April 1, 2026, 12:15 a.m. |
Created at: March 30, 2026, 5:23 p.m.