Triple
T2215630
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. Bennet (Benjamin Whitrow) |
E48024
|
entity |
| Predicate | primaryThemeAssociation |
P36853
|
FINISHED |
| Object | marriage and family dynamics |
—
|
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 and family dynamics | Statement: [Mr. Bennet (Benjamin Whitrow), primaryThemeAssociation, marriage and family dynamics]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryThemeAssociation Context triple: [Mr. Bennet (Benjamin Whitrow), primaryThemeAssociation, marriage and family dynamics]
-
A.
primaryUniformAssociation
Indicates that one entity is designated as the main or primary uniform associated with another entity.
-
B.
primaryColorPalette
Indicates the set of main or dominant colors associated with an entity, typically used as its core color scheme.
-
C.
primaryColour
Indicates that one entity is the main or dominant color characteristic of another entity.
-
D.
primaryLogoTheme
Indicates the main stylistic or visual design theme associated with a logo.
-
E.
primaryType
Indicates the main or most fundamental category or classification assigned to an entity, distinguishing it from any secondary or auxiliary types.
- 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_69abbff11574819091d1b50d637ae767 |
completed | March 7, 2026, 6:04 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.