Triple
T31882322
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Caesar |
E813912
|
entity |
| Predicate | styleInfluenceOn |
P173356
|
FINISHED |
| Object | Latin prose |
—
|
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: Latin prose | Statement: [Caesar, styleInfluenceOn, Latin prose]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: styleInfluenceOn Context triple: [Caesar, styleInfluenceOn, Latin prose]
-
A.
influencedFashionTrend
Indicates that one entity caused or contributed to a change or direction in another entity’s fashion style or prevailing clothing trends.
-
B.
designInfluenceOn
Indicates that one design, designer, or design-related factor has an effect on shaping, guiding, or altering another design or design outcome.
-
C.
inTheStyleOf
Indicates that one entity is created, performed, or presented in a manner that imitates or closely resembles the characteristic style of another entity.
-
D.
influenceOf
chosen
Indicates that one entity affects, shapes, or alters the state, behavior, or properties of another entity.
-
E.
styleTendsTo
Indicates that one style is generally inclined or likely to develop, appear, or be adopted in the direction of another style.
- 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_69f348ed74bc81909846aaa6a3c7318c |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6c8159edc8190b1c87015e0c820e8 |
completed | May 3, 2026, 3:59 a.m. |
| PD | Predicate disambiguation | batch_69f6c3f42fbc8190a06eb1044c9e6094 |
completed | May 3, 2026, 3:41 a.m. |
Created at: April 30, 2026, 11:56 p.m.