Triple
T14530008
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Gentleman from New Orleans |
E340883
|
entity |
| Predicate | settingCulture |
P387
|
FINISHED |
| Object | Louisiana Creole culture |
—
|
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: Louisiana Creole culture | Statement: [The Gentleman from New Orleans, settingCulture, Louisiana Creole culture]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: settingCulture Context triple: [The Gentleman from New Orleans, settingCulture, Louisiana Creole culture]
-
A.
supportsCulture
Indicates that one entity actively promotes, sustains, or enhances the cultural practices, values, or expressions associated with another entity.
-
B.
locale
chosen
Indicates that one entity is the place, setting, or geographic area in which another entity exists, occurs, or is situated.
-
C.
languageZone
Indicates the linguistic region or area in which a language is predominantly used or officially recognized.
-
D.
replacedCulture
Indicates that one culture has been supplanted or taken the place of another culture.
-
E.
localeType
Indicates the classification or category of a locale (such as region, city, or venue type) that characterizes the kind of place involved in the relationship.
- 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_69d822dac79c8190a84a073f3cbaced5 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dea052d01c81909c8592c351be6f35 |
completed | April 14, 2026, 8:15 p.m. |
| PD | Predicate disambiguation | batch_69de5c518fc08190a6ce4d8be05c4c5d |
completed | April 14, 2026, 3:25 p.m. |
Created at: April 10, 2026, 1:22 a.m.