Triple
T7497753
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ned Dorsey |
E177175
|
entity |
| Predicate | narrativeThemeAssociation |
P51756
|
FINISHED |
| Object | marriage 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 of convenience | Statement: [Ned Dorsey, narrativeThemeAssociation, marriage of convenience]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: narrativeThemeAssociation Context triple: [Ned Dorsey, narrativeThemeAssociation, marriage of convenience]
-
A.
narrativeThemeInvolvement
Indicates that an entity participates in or contributes to a particular narrative theme within a story or discourse.
-
B.
majorThemeAssociation
Indicates that one entity is associated with another as a primary or central theme.
-
C.
narrativeMotif
Indicates a recurring thematic element, pattern, or situation that appears across one or more narratives and helps structure or convey their underlying meanings.
-
D.
literaryThemeInvolvement
Indicates the involvement or presence of a particular literary theme within a work, passage, or character arc.
-
E.
narrativeConnection
chosen
Indicates a meaningful relationship between elements within a narrative, such as events, characters, or scenes, that links them in terms of plot, causality, or thematic continuity.
- 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_69c69f2696688190915a8458f2398211 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f81b431481908214b69c6c8d83bc |
completed | March 27, 2026, 9:35 p.m. |
| PD | Predicate disambiguation | batch_69c6f4d266d88190982cf5d2ee2e9564 |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:44 p.m.