Triple
T33478350
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charlotte Lucas |
E857386
|
entity |
| Predicate | viewsOnMarriage |
P161039
|
FINISHED |
| Object | marriage as a matter of convenience |
—
|
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: marriage as a matter of convenience | Statement: [Charlotte Lucas, viewsOnMarriage, marriage as a matter of convenience]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: viewsOnMarriage Context triple: [Charlotte Lucas, viewsOnMarriage, marriage as a matter of convenience]
-
A.
discussesMarriageWith
Indicates that one entity engages in conversation or dialogue with another specifically about marriage-related topics or decisions.
-
B.
hasAttitudeTowardMarriage
Indicates that an entity holds a particular opinion, feeling, or stance regarding the institution or concept of marriage.
-
C.
marriageControversyInvolved
Indicates that an entity is involved in a dispute, scandal, or public controversy related to a marriage.
-
D.
regardsMarriageAs
chosen
Indicates that one entity holds a particular view, attitude, or evaluative stance toward the institution or concept of marriage.
-
E.
involvesMarriage
Indicates that the relationship or action includes or is characterized by a marriage between the involved entities.
- 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_69f3497472508190b300ebd3fd402367 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6e52c1a848190b35743f9e5361969 |
completed | May 3, 2026, 6:03 a.m. |
| PD | Predicate disambiguation | batch_69f6e3da41948190a4cfe866ce184f73 |
completed | May 3, 2026, 5:57 a.m. |
Created at: May 1, 2026, 1:38 a.m.