Triple
T32367848
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jacobson School (historical) in Seesen |
E827042
|
entity |
| Predicate | hasSecularStudiesEmphasis |
P2316
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Jacobson School (historical) in Seesen, hasSecularStudiesEmphasis, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSecularStudiesEmphasis Context triple: [Jacobson School (historical) in Seesen, hasSecularStudiesEmphasis, true]
-
A.
hasSecularAspects
chosen
Indicates that something includes or exhibits non-religious, worldly, or secular characteristics or dimensions.
-
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.
hasSecularStatus
Indicates that an entity holds a non-religious, secular standing or classification within a given context.
-
D.
hasModeOfStudy
Indicates the method or format by which an entity (typically a learner) undertakes their studies, such as full-time, part-time, online, or in-person.
-
E.
hasSecularUse
Indicates that something is used in a non-religious, worldly, or secular context or purpose.
- 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_69f349166d548190887b412fe908e2f4 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69ff4de66ba481908e7184b3cf9d4d2d |
completed | May 9, 2026, 3:08 p.m. |
| PD | Predicate disambiguation | batch_69ff4c702a5881909c6684c74807e945 |
completed | May 9, 2026, 3:02 p.m. |
Created at: May 1, 2026, 12:50 a.m.