Triple
T2216733
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kristin Scott Thomas as Katherine Clifton |
E48049
|
entity |
| Predicate | centralThemeContribution |
P36866
|
FINISHED |
| Object | adultery |
—
|
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: adultery | Statement: [Kristin Scott Thomas as Katherine Clifton, centralThemeContribution, adultery]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: centralThemeContribution Context triple: [Kristin Scott Thomas as Katherine Clifton, centralThemeContribution, adultery]
-
A.
hasCentralTheme
Indicates that one entity serves as the primary or dominant theme or subject matter of another entity.
-
B.
themeFor
Indicates that something serves as the central subject, topic, or focus for another thing (such as an event, work, or activity).
-
C.
editorialTheme
Indicates a relationship where an editorial work is associated with a specific overarching theme or subject focus that guides its content and presentation.
-
D.
subtheme
Indicates that one topic or concept functions as a more specific, subordinate theme within a broader overarching theme.
-
E.
thematicArea
Indicates the subject or item is associated with, or falls under, a particular thematic area or topic of focus.
- F. None of above. chosen
Provenance (4 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_69a88aa1ee708190862c8c378c41e9eb |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc00f4c3881909d03301fcdfa8b67 |
completed | March 7, 2026, 6:05 a.m. |
| PD | Predicate disambiguation | batch_69abbdaa26d48190860c33fd464c4845 |
completed | March 7, 2026, 5:54 a.m. |
| PDg | Predicate description generation | batch_69abbf0c2b8881908553eed5be17a9c2 |
completed | March 7, 2026, 6 a.m. |
Created at: March 4, 2026, 7:46 p.m.