Triple
T14269686
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ra’iisul wasaaraha Jabuuti |
E353745
|
entity |
| Predicate | hasCounterpartTitleInArabic |
P33894
|
FINISHED |
| Object | رئيس وزراء جيبوتي |
—
|
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: رئيس وزراء جيبوتي | Statement: [Ra’iisul wasaaraha Jabuuti, hasCounterpartTitleInArabic, رئيس وزراء جيبوتي]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCounterpartTitleInArabic Context triple: [Ra’iisul wasaaraha Jabuuti, hasCounterpartTitleInArabic, رئيس وزراء جيبوتي]
-
A.
hasCounterpart
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.
hasNameInArabic
Indicates that an entity is associated with a specific name expressed in the Arabic language.
-
D.
hasCounterpartNickname
Indicates that one entity is used as an alternative or counterpart nickname for another entity.
-
E.
titleInArabic
chosen
Indicates that an entity’s title is expressed in the Arabic language.
- 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_69d8278d25148190abf1a8c8f5f533ad |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de657fe6708190b41de48c43cff647 |
completed | April 14, 2026, 4:04 p.m. |
| PD | Predicate disambiguation | batch_69de2a7d586c8190846ff242bbf5ac53 |
completed | April 14, 2026, 11:52 a.m. |
Created at: April 10, 2026, 1:10 a.m.