Triple
T10736512
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seder Nezikin |
E253207
|
entity |
| Predicate | subjectOfStudyIn |
P70296
|
FINISHED |
| Object | yeshiva curriculum |
—
|
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: yeshiva curriculum | Statement: [Seder Nezikin, subjectOfStudyIn, yeshiva curriculum]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subjectOfStudyIn Context triple: [Seder Nezikin, subjectOfStudyIn, yeshiva curriculum]
-
A.
isSubjectOfStudy
chosen
Indicates that an entity is the focus or topic being examined, researched, or analyzed in a study or investigation.
-
B.
hasSubjectOfStudy
Indicates that an entity (such as a person or organization) focuses on, researches, or specializes in a particular field or topic of study.
-
C.
widelyStudiedIn
Indicates that something has been extensively researched, analyzed, or examined within a particular field, domain, or context.
-
D.
regionOfStudy
Indicates the academic or research area that is the focus of someone’s study or investigation.
-
E.
dimensionOfStudy
Indicates the specific field, aspect, or perspective that characterizes or structures a particular study or research activity.
- 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_69d6aa5e51e8819095f06881cecf152e |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d71022aee0819091a5790d3dee6777 |
completed | April 9, 2026, 2:34 a.m. |
| PD | Predicate disambiguation | batch_69d6f309a44881908e49e3ba478c35b4 |
completed | April 9, 2026, 12:30 a.m. |
Created at: April 8, 2026, 9:14 p.m.