Triple
T16895246
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manuel |
E424281
|
entity |
| Predicate | culturalStereotype |
P97370
|
FINISHED |
| Object | exaggerated Spanish waiter |
—
|
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: exaggerated Spanish waiter | Statement: [Manuel, culturalStereotype, exaggerated Spanish waiter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: culturalStereotype Context triple: [Manuel, culturalStereotype, exaggerated Spanish waiter]
-
A.
notableStereotype
chosen
Indicates that a commonly recognized stereotype is associated with the subject in relation to the object.
-
B.
popularCultureTrait
Indicates that an entity exhibits a characteristic, behavior, or element that is commonly recognized or influential within popular culture.
-
C.
opposingCulture
Indicates a relationship where one culture stands in opposition to, conflicts with, or resists the values, practices, or influence of another culture.
-
D.
hasRacialStereotypes
Indicates that one entity portrays, attributes, or associates racial stereotypes with another entity.
-
E.
ethnicCategoryIn
Indicates that an entity belongs to or is classified within a specified ethnic category in a given context.
- 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_69d889da3e8c8190a2b118f383f0beac |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e3c8d7aec88190888f13601acbcd77 |
completed | April 18, 2026, 6:09 p.m. |
| PD | Predicate disambiguation | batch_69e32b90ec3c819099c51bb7baf2984c |
completed | April 18, 2026, 6:58 a.m. |
Created at: April 10, 2026, 5:29 a.m.