Triple
T13968726
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Younes and Soraya Nazarian Library |
E335994
|
entity |
| Predicate | hasLanguageOfMaterials |
P32511
|
FINISHED |
| Object | Hebrew |
—
|
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: Hebrew | Statement: [Younes and Soraya Nazarian Library, hasLanguageOfMaterials, Hebrew]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLanguageOfMaterials Context triple: [Younes and Soraya Nazarian Library, hasLanguageOfMaterials, Hebrew]
-
A.
languageOfMaterial
chosen
Indicates the language in which a given material, resource, or content is expressed or presented.
-
B.
materialUsed
Indicates that one entity is made from, incorporates, or utilizes the other entity as its material or substance.
-
C.
materialFormedIn
Indicates that a material is created, produced, or formed within a specified process, environment, or context.
-
D.
hasLanguageOn
Indicates that an entity uses or is associated with a particular language in a specific context, medium, or location.
-
E.
featuresMaterialFrom
Indicates that one entity incorporates, contains, or is composed of material originating from another entity.
- 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_69d81c61f3508190aaf2ca0dc0002c59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2e8daeac8190aadd4b3b60222482 |
completed | April 14, 2026, 12:09 p.m. |
| PD | Predicate disambiguation | batch_69dd465a21408190b912a42c50ffa0d9 |
completed | April 13, 2026, 7:39 p.m. |
Created at: April 9, 2026, 10:18 p.m.