Triple
T28211440
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | House of Representatives elections (usually concurrent) |
E711178
|
entity |
| Predicate | mainLanguageUsed |
P58450
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [House of Representatives elections (usually concurrent), mainLanguageUsed, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainLanguageUsed Context triple: [House of Representatives elections (usually concurrent), mainLanguageUsed, English]
-
A.
languageUsedAs
Indicates that one language is employed in a specific role, function, or context relative to another entity or situation.
-
B.
languagesUsed
chosen
Indicates that one entity uses, employs, or is expressed in one or more languages associated with the other entity.
-
C.
languageOfPrimaryProgramming
Indicates the programming language that is primarily used to implement or develop a given entity.
-
D.
languageOfMostProgramming
Indicates that the specified language is the one in which the largest number of programming activities or programs for a given entity are conducted.
-
E.
primaryLanguageOfProject
Indicates the main natural or programming language used in a given project.
- 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_69efb51cb5288190818c1f63a266af11 |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_69f65876c52c8190bc889c7a67bd07f3 |
completed | May 2, 2026, 8:03 p.m. |
| PD | Predicate disambiguation | batch_69f6575d89788190aca478e4aea05a65 |
completed | May 2, 2026, 7:58 p.m. |
Created at: April 27, 2026, 10:39 p.m.