Triple
T34234148
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siobhan Roy |
E878284
|
entity |
| Predicate | hasMaritalConflict |
P50123
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Siobhan Roy, hasMaritalConflict, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMaritalConflict Context triple: [Siobhan Roy, hasMaritalConflict, true]
-
A.
maritalIssue
chosen
Indicates a relationship where there is conflict, dissatisfaction, or significant strain within a marital or committed partnership.
-
B.
hasFatherDaughterConflict
Indicates a relationship in which a father and daughter are experiencing tension, disagreement, or unresolved conflict between them.
-
C.
effectOnMaritalRelations
Indicates how an action, event, or condition influences the quality, stability, or dynamics of marital relationships between partners.
-
D.
marriageControversyInvolved
Indicates that an entity is involved in a dispute, scandal, or public controversy related to a marriage.
-
E.
hasMaritalFunction
Indicates that one entity serves a role or performs a function within the context of a marital relationship or institution.
- 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_69f349b22d8c819096b22df268382aa9 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a017d27e184819094638c3cf6876de4 |
completed | May 11, 2026, 6:54 a.m. |
| PD | Predicate disambiguation | batch_6a017c785a44819083111384b55769e9 |
completed | May 11, 2026, 6:51 a.m. |
Created at: May 1, 2026, 1:56 a.m.