Triple
T10516766
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bertha Mason |
E248053
|
entity |
| Predicate | criticalInterpretation |
P94331
|
FINISHED |
| Object | figure of feminist resistance |
—
|
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: figure of feminist resistance | Statement: [Bertha Mason, criticalInterpretation, figure of feminist resistance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: criticalInterpretation Context triple: [Bertha Mason, criticalInterpretation, figure of feminist resistance]
-
A.
criticalApproach
Indicates that an entity employs an analytical, questioning, or evaluative method toward another entity, concept, or work.
-
B.
criticality
Indicates the degree of importance, urgency, or potential impact associated with an entity, condition, or situation within a given context.
-
C.
criticalReception
Indicates how a work, performance, or product is evaluated and responded to by critics or professional reviewers.
-
D.
criticalStrip
Indicates that something lies within the critical strip, the region of complex values whose real parts fall between 0 and 1.
-
E.
criticalReputation
Indicates that an entity is regarded by critics with a particular level or type of esteem, evaluation, or standing based on critical assessments.
- F. None of above. chosen
Provenance (4 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_69d381c4aa948190942e1d803143fb0e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d509cc58b081908ef15aa89b396db6 |
completed | April 7, 2026, 1:42 p.m. |
| PD | Predicate disambiguation | batch_69d4fb919ea08190bcc1193e2014d437 |
completed | April 7, 2026, 12:41 p.m. |
| PDg | Predicate description generation | batch_69d4fe058fcc81909428137d9ffd6d90 |
completed | April 7, 2026, 12:52 p.m. |
Created at: April 6, 2026, 12:28 p.m.