Triple
T10794010
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Caroline Bingley |
E254655
|
entity |
| Predicate | demeanorToward |
P86122
|
FINISHED |
| Object | condescending toward Elizabeth Bennet |
—
|
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: condescending toward Elizabeth Bennet | Statement: [Caroline Bingley, demeanorToward, condescending toward Elizabeth Bennet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: demeanorToward Context triple: [Caroline Bingley, demeanorToward, condescending toward Elizabeth Bennet]
-
A.
hasManner
chosen
Indicates the way, style, or method in which an action is performed or a relation is carried out.
-
B.
behaviorNear
Indicates that one entity exhibits a behavior or action in close spatial proximity to another entity.
-
C.
aspect
Indicates a specific temporal phase or manner in which an action, event, or state unfolds or is viewed (e.g., ongoing, completed, habitual).
-
D.
moralAttitude
Indicates a subject’s evaluative stance or judgment about the moral rightness or wrongness of another entity, action, or situation.
-
E.
moralTone
Indicates the evaluative moral quality or ethical character expressed in or associated with an action, statement, or situation.
- 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_69d6aa609f008190a294200aefcb7bd5 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d732f878648190be5e25c56a7511cf |
completed | April 9, 2026, 5:02 a.m. |
| PD | Predicate disambiguation | batch_69d6f316940c819092a96c429629fdef |
completed | April 9, 2026, 12:30 a.m. |
Created at: April 8, 2026, 9:17 p.m.