Triple
T32472713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deputy Prime Minister of Iran |
E829887
|
entity |
| Predicate | nativeLanguageOfCountry |
P119790
|
FINISHED |
| Object | Persian |
—
|
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: Persian | Statement: [Deputy Prime Minister of Iran, nativeLanguageOfCountry, Persian]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nativeLanguageOfCountry Context triple: [Deputy Prime Minister of Iran, nativeLanguageOfCountry, Persian]
-
A.
nativeLanguage
Indicates the language that a person or entity originally learned and uses as their primary or first language.
-
B.
nationalLanguageSpoken
Indicates that a particular language is officially recognized and commonly used as a national language within a given country or region.
-
C.
primaryLanguageCountry
chosen
Indicates that a given language is the main or officially predominant language used within a particular country.
-
D.
countryOfLanguage
Indicates that a particular language is officially or predominantly used within a specified country.
-
E.
languageOfSurroundingCountry
Indicates that a language is the primary or commonly used language in the country surrounding a given place or region.
- 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_69f3491ee87c81908cbf5890079c2af6 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6cd126fcc8190aa1f1f146e45ec0c |
completed | May 3, 2026, 4:20 a.m. |
| PD | Predicate disambiguation | batch_69f6cc1470808190b70cdfd7a6395670 |
completed | May 3, 2026, 4:16 a.m. |
Created at: May 1, 2026, 12:57 a.m.