Triple
T19905835
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cotton Whigs |
E478411
|
entity |
| Predicate | positionOnSectionalism |
P137771
|
FINISHED |
| Object | sought to reduce sectional tensions |
—
|
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: sought to reduce sectional tensions | Statement: [Cotton Whigs, positionOnSectionalism, sought to reduce sectional tensions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: positionOnSectionalism Context triple: [Cotton Whigs, positionOnSectionalism, sought to reduce sectional tensions]
-
A.
sectionalOpposition
Indicates a relationship where two or more sections or segments are positioned or oriented in opposition or contrast to each other.
-
B.
politicalSide
Indicates the political alignment or ideological position that one entity holds in relation to political spectra or groupings.
-
C.
positionOnCentralization
Indicates the degree to which an entity favors centralized versus decentralized control, authority, or decision-making.
-
D.
nationalAlignment
Indicates how closely an entity’s interests, policies, or actions are aligned with those of a particular nation or national government.
-
E.
positionOnReform
Indicates a stance or viewpoint that an entity holds regarding a particular reform or set of reforms.
- 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_69d8e520682081909892916424699bd5 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65946916881909c3f52208c07aa64 |
completed | April 20, 2026, 4:50 p.m. |
| PD | Predicate disambiguation | batch_69e537ecda248190895c96afb6243823 |
completed | April 19, 2026, 8:15 p.m. |
| PDg | Predicate description generation | batch_69e543c136b081909cab9394b958390a |
completed | April 19, 2026, 9:06 p.m. |
Created at: April 10, 2026, 1:52 p.m.