Triple
T11438548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ʿAmr ibn ʿUthmān ibn Qanbar |
E271074
|
entity |
| Predicate | languageProficiency |
P741
|
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: [ʿAmr ibn ʿUthmān ibn Qanbar, languageProficiency, Arabic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageProficiency Context triple: [ʿAmr ibn ʿUthmān ibn Qanbar, languageProficiency, Arabic]
-
A.
languageCapacity
Indicates the extent to which an entity is able to understand, produce, or otherwise use language.
-
B.
eligibleLanguage
Indicates that a particular language satisfies the required conditions to be considered valid or allowed in a given context.
-
C.
languagesSpoken
chosen
Indicates that an entity is able to communicate using one or more specified languages.
-
D.
languageOfAssessment
Indicates that a specified language is used as the medium of assessment (e.g., for tests, evaluations, or examinations) for a given entity.
-
E.
languageOutcome
Indicates the resulting language or linguistic state that emerges from a given process, action, or interaction.
- 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_69d6aadeef688190874bcecd88b3dd9b |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8088711ec8190afae9f4d9f2a11ca |
completed | April 9, 2026, 8:13 p.m. |
| PD | Predicate disambiguation | batch_69d7e7162b288190a0bfb89f7eb747c7 |
completed | April 9, 2026, 5:51 p.m. |
Created at: April 8, 2026, 9:35 p.m.