Triple
T7756520
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. Prime Minister |
E175910
|
entity |
| Predicate | hasCounterpartInRussian |
P6587
|
FINISHED |
| Object | Господин Председатель Правительства |
—
|
LITERAL 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: Господин Председатель Правительства | Statement: [Mr. Prime Minister, hasCounterpartInRussian, Господин Председатель Правительства]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCounterpartInRussian Context triple: [Mr. Prime Minister, hasCounterpartInRussian, Господин Председатель Правительства]
-
A.
hasCounterpart
chosen
Indicates that one entity corresponds to, matches, or serves as an equivalent or parallel version of another entity.
-
B.
counterpartEnglishName
Indicates that an entity has a corresponding counterpart whose name is given in English.
-
C.
nameInRussian
Indicates that an entity is known or referred to by a specific name when expressed in the Russian language.
-
D.
hasCognate
Indicates that two linguistic forms in different languages share a common historical origin, typically descending from the same ancestral word.
-
E.
alsoTranslatedAs
Indicates that something has an alternative translation or rendering in another language or form.
- F. None of above.
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_69c6996180088190832e38e8d83ff54a |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c705257ca08190a78c592a1e616da8 |
completed | March 27, 2026, 10:31 p.m. |
| PD | Predicate disambiguation | batch_69c7016df2b08190b2330a2010691431 |
completed | March 27, 2026, 10:15 p.m. |
Created at: March 27, 2026, 4:08 p.m.