Triple
T14403470
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christina Drayton |
E357131
|
entity |
| Predicate | confrontsIssue |
P13650
|
FINISHED |
| Object | daughter's engagement to a Black man |
—
|
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: daughter's engagement to a Black man | Statement: [Christina Drayton, confrontsIssue, daughter's engagement to a Black man]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: confrontsIssue Context triple: [Christina Drayton, confrontsIssue, daughter's engagement to a Black man]
-
A.
confronts
Indicates that one entity directly faces and challenges another, often in opposition or dispute.
-
B.
facingIssue
chosen
Indicates that an entity is currently experiencing, encountering, or dealing with a problem, difficulty, or obstacle.
-
C.
involvesIssue
Indicates that an action, event, or entity is related to, concerns, or includes a particular issue.
-
D.
helpsCharacterConfront
Indicates that one character actively supports or enables another character in facing and dealing with a difficult issue, fear, or challenge.
-
E.
addressedConflictWith
Indicates that one entity has taken action to confront, manage, or resolve a conflict involving another entity.
- 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_69d827927c988190ad98bb0360981783 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de90860ae481908e175decda8624d5 |
completed | April 14, 2026, 7:07 p.m. |
| PD | Predicate disambiguation | batch_69de2aa024c48190805df6a9d63deb10 |
completed | April 14, 2026, 11:53 a.m. |
Created at: April 10, 2026, 1:17 a.m.