Triple
T10644039
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chester Beatty Library |
E250793
|
entity |
| Predicate | hasLanguageCoverage |
P35567
|
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: [Chester Beatty Library, hasLanguageCoverage, Arabic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLanguageCoverage Context triple: [Chester Beatty Library, hasLanguageCoverage, Arabic]
-
A.
languageOfCoverage
Indicates the language in which the coverage, such as reporting or documentation about something, is expressed.
-
B.
hasLanguageOn
Indicates that an entity uses or is associated with a particular language in a specific context, medium, or location.
-
C.
hasLanguages
chosen
Indicates that an entity is associated with one or more languages it uses, supports, or is expressed in.
-
D.
hasLanguageRepresentation
Indicates that an entity is expressed, encoded, or represented using a particular natural or formal language.
-
E.
hasLanguageStatus
Indicates that an entity has a particular status or condition regarding its language use, recognition, or classification.
- 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_69d6aa5a4c4881908f39be6efe5981e5 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6dfd04ca88190ac4fffd13c1f33a8 |
completed | April 8, 2026, 11:08 p.m. |
| PD | Predicate disambiguation | batch_69d6dd83b114819098e84dc658e82d7e |
completed | April 8, 2026, 10:58 p.m. |
Created at: April 8, 2026, 9:05 p.m.