Triple
T32191784
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Janet Tyler |
E822274
|
entity |
| Predicate | subjectOfTheme |
P81245
|
FINISHED |
| Object | critique of oppressive beauty standards |
—
|
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: critique of oppressive beauty standards | Statement: [Janet Tyler, subjectOfTheme, critique of oppressive beauty standards]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subjectOfTheme Context triple: [Janet Tyler, subjectOfTheme, critique of oppressive beauty standards]
-
A.
subjectOfCatalog
Indicates that something is the primary focus or entry described within a catalog or cataloging record.
-
B.
subjectOfDescription
Indicates that the subject is the main entity being described or characterized in a given context or statement.
-
C.
titleSubjectOf
Indicates that a title (such as a book, article, or work) is about or primarily concerns a particular subject.
-
D.
subjectOfIntroduction
Indicates that one entity is the topic or focus being introduced by another entity.
-
E.
thematicConcept
chosen
Indicates that one entity embodies, expresses, or is centrally concerned with a particular underlying theme or conceptual idea represented by the other 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_69f3490819cc81909bae1f8ce99423c5 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fcab6e888881908ca9e18660928a40 |
completed | May 7, 2026, 3:10 p.m. |
| PD | Predicate disambiguation | batch_69fc4562a5b88190bad48f083a6dcdfa |
completed | May 7, 2026, 7:55 a.m. |
Created at: May 1, 2026, 12:35 a.m.