Triple
T22959124
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siwi language |
E570843
|
entity |
| Predicate | hasDominantNeighborLanguage |
P150386
|
FINISHED |
| Object | Arabic language |
—
|
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: Arabic language | Statement: [Siwi language, hasDominantNeighborLanguage, Arabic language]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDominantNeighborLanguage Context triple: [Siwi language, hasDominantNeighborLanguage, Arabic language]
-
A.
hasNeighboringLanguages
Indicates that two languages are geographically or regionally adjacent to each other in their areas of use.
-
B.
laterLanguageDominant
Indicates that one language becomes the dominant or primary language for an entity at a later point in time, after another language previously held that role.
-
C.
hasPrimaryLanguageNearby
Indicates that an entity is associated with a primary language that is predominantly used or present in its immediate geographic or contextual vicinity.
-
D.
hasSecondaryLanguageNearby
Indicates that an entity has at least one secondary language present or used in its immediate vicinity or surrounding context.
-
E.
educationLanguageDominant
Indicates that one language is the primary or most influential language used in a person’s education.
- F. None of above. chosen
Provenance (4 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_69e245b212a88190b5259caf51606084 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f181f2ce9c8190977f146771816341 |
completed | April 29, 2026, 3:58 a.m. |
| PD | Predicate disambiguation | batch_69ef3b882e708190b0eb0c87021c75b8 |
completed | April 27, 2026, 10:33 a.m. |
| PDg | Predicate description generation | batch_69ef538a115081908982597f79355840 |
completed | April 27, 2026, 12:16 p.m. |
Created at: April 17, 2026, 3:47 p.m.