Triple
T1458890
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Colored Town |
E31461
|
entity |
| Predicate | socialContext |
P8198
|
FINISHED |
| Object | racial segregation 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: racial segregation in the United States | Statement: [Colored Town, socialContext, racial segregation in the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: socialContext Context triple: [Colored Town, socialContext, racial segregation in the United States]
-
A.
socialBackground
Indicates a relationship where one entity’s social origin, class, or upbringing context is associated with or characterizes another entity.
-
B.
socialComposition
chosen
Indicates the makeup or distribution of different social groups or categories within a population or community.
-
C.
socialContribution
Indicates that an entity engages in actions or provides resources that benefit society or a community beyond its own direct interests.
-
D.
socialBase
Indicates a foundational social relationship or structure that underlies or supports interactions between entities.
-
E.
socialFeature
Indicates that one entity provides or participates in a social interaction capability or function involving other entities.
- 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_69a49917dfc081909acdbdf5d684f1ef |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c59c1c288190be08064f2d351b2b |
completed | March 1, 2026, 11:02 p.m. |
| PD | Predicate disambiguation | batch_69a4c47ec5108190b1772237f2e5d90b |
completed | March 1, 2026, 10:58 p.m. |
Created at: March 1, 2026, 8 p.m.