Triple
T33297807
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bisbee riot of 1919 |
E852494
|
entity |
| Predicate | hasRacialContext |
P159747
|
FINISHED |
| Object | white–Black racial conflict in the United States |
—
|
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: white–Black racial conflict in the United States | Statement: [Bisbee riot of 1919, hasRacialContext, white–Black racial conflict in the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRacialContext Context triple: [Bisbee riot of 1919, hasRacialContext, white–Black racial conflict in the United States]
-
A.
hasRacialTheme
chosen
Indicates that something contains or centers on themes, issues, or representations related to race or racial identity.
-
B.
hasRacialStereotypes
Indicates that one entity portrays, attributes, or associates racial stereotypes with another entity.
-
C.
hasAssociatedRace
Indicates that an entity is linked to or characterized by a particular race or racial classification.
-
D.
hasFeatureRace
Indicates that an entity includes, offers, or is associated with a specific race-related feature or race event.
-
E.
hasRace
Indicates that an entity possesses or is characterized by a particular race or racial classification.
- 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_69f34966ed4c81908dc9dda82d8c7fe3 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fce28d6c3081908bf76f5db63ecf68 |
completed | May 7, 2026, 7:05 p.m. |
| PD | Predicate disambiguation | batch_69fce12d2f08819082134b5eb3db6a24 |
completed | May 7, 2026, 6:59 p.m. |
Created at: May 1, 2026, 1:33 a.m.