Triple
T14594078
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Member of the Knesset |
E342513
|
entity |
| Predicate | secondaryWorkLanguage |
P9103
|
FINISHED |
| Object | Arabic |
—
|
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 | Statement: [Member of the Knesset, secondaryWorkLanguage, Arabic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: secondaryWorkLanguage Context triple: [Member of the Knesset, secondaryWorkLanguage, Arabic]
-
A.
hasSecondaryLanguage
chosen
Indicates that an entity possesses or uses a secondary language in addition to its primary language.
-
B.
primaryLanguageSide2
Indicates that the second entity in the relationship uses or is associated with the primary language specified.
-
C.
laterSecondaryLanguageOfAdministration
Indicates that one language served as a subsequent or later secondary language used for administrative purposes in relation to another language.
-
D.
hasSecondaryNationalLanguage
Indicates that an entity possesses an officially recognized secondary national language in addition to its primary national language.
-
E.
hasSecondaryLanguageNearby
Indicates that an entity has at least one secondary language present or used in its immediate vicinity or surrounding context.
- 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_69d822ddc0f081909cd8163c7de298cd |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb43480d8819084a707e56da2c237 |
completed | April 14, 2026, 9:40 p.m. |
| PD | Predicate disambiguation | batch_69de656a953481909a4645b004c40de7 |
completed | April 14, 2026, 4:03 p.m. |
Created at: April 10, 2026, 1:24 a.m.