Triple
T15113338
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Colonel Reb |
E360969
|
entity |
| Predicate | hasEthnicStereotype |
P65260
|
FINISHED |
| Object | Old white Southern man |
—
|
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: Old white Southern man | Statement: [Colonel Reb, hasEthnicStereotype, Old white Southern man]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEthnicStereotype Context triple: [Colonel Reb, hasEthnicStereotype, Old white Southern man]
-
A.
hasRacialStereotypes
chosen
Indicates that one entity portrays, attributes, or associates racial stereotypes with another entity.
-
B.
hasEthnicCharacteristic
Indicates that an entity possesses or is associated with a particular ethnic characteristic or identity.
-
C.
notableStereotype
Indicates that a commonly recognized stereotype is associated with the subject in relation to the object.
-
D.
hasEthnicInfluence
Indicates that one entity has a cultural, traditional, or ethnic impact on, or contributes to shaping the ethnic character of, another entity.
-
E.
holderEthnicity
Indicates the ethnic background or group to which the holder of something (e.g., a document, account, or item) belongs.
- 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_69d85a0491ec8190830960be8fafb994 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0058d786c8190937c6819255c01bd |
completed | April 15, 2026, 9:39 p.m. |
| PD | Predicate disambiguation | batch_69deb96c1d9c81909351558ed97bc5b7 |
completed | April 14, 2026, 10:02 p.m. |
Created at: April 10, 2026, 3:05 a.m.