Triple
T10389816
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sex and Secularism |
E244860
|
entity |
| Predicate | authorAcademicField |
P43754
|
FINISHED |
| Object | history |
—
|
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: history | Statement: [Sex and Secularism, authorAcademicField, history]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: authorAcademicField Context triple: [Sex and Secularism, authorAcademicField, history]
-
A.
hasAcademicAffiliation
Indicates that an entity is formally associated with an academic institution, such as through employment, enrollment, or official collaboration.
-
B.
subjectAffiliation
Indicates that a subject is formally associated or connected with a particular organization, group, or institution.
-
C.
regionOfAcademicFocus
chosen
Indicates the academic subject area or discipline that an entity (such as a person or program) primarily concentrates on or specializes in.
-
D.
hasAcademicFieldGroup
Indicates that an entity is associated with, or belongs to, a particular group or category of academic fields.
-
E.
academicBody
Indicates a formal organizational relationship in which an entity functions as an academic institution or governing academic unit associated with 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_69d381b5116081908d85227bab6d3c0c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e9b40dd8819080ac839487020a44 |
completed | April 7, 2026, 11:25 a.m. |
| PD | Predicate disambiguation | batch_69d4dfb0e7a88190bec0b7a52c70dfe2 |
completed | April 7, 2026, 10:42 a.m. |
Created at: April 6, 2026, 12:05 p.m.