Triple
T11602031
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Basil Ransom |
E275153
|
entity |
| Predicate | thematicContrastWith |
P7994
|
FINISHED |
| Object | feminism |
—
|
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: feminism | Statement: [Basil Ransom, thematicContrastWith, feminism]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: thematicContrastWith Context triple: [Basil Ransom, thematicContrastWith, feminism]
-
A.
themeContrast
chosen
Indicates a relationship where two themes are compared or opposed to highlight their differences or tension.
-
B.
dramaticContrastWith
Indicates that one entity is presented in a way that sharply emphasizes differences in tone, style, or impact when compared with another entity.
-
C.
achievesContrast
Indicates that one entity creates or enhances a visual or conceptual difference relative to another entity.
-
D.
exploresContrastBetween
Indicates a relationship in which one entity examines, highlights, or analyzes the differences or oppositions between two or more entities, ideas, or situations.
-
E.
traditionalContrastWith
Indicates a relationship where one tradition, practice, or belief is explicitly set in opposition or difference to another, highlighting their contrasting characteristics.
- 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_69d6aae6b14c81908dc5a74bad7591f9 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8954daa908190a8d532e43aa4a881 |
completed | April 10, 2026, 6:14 a.m. |
| PD | Predicate disambiguation | batch_69d85dd20d188190863d1190d4c16048 |
completed | April 10, 2026, 2:17 a.m. |
Created at: April 8, 2026, 9:38 p.m.