Triple
T219774
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jim Crow laws |
E4187
|
entity |
| Predicate | segregated |
P1175
|
FINISHED |
| Object | public schools |
—
|
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: public schools | Statement: [Jim Crow laws, segregated, public schools]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: segregated Context triple: [Jim Crow laws, segregated, public schools]
-
A.
separates
chosen
Indicates that one entity divides, parts, or keeps other entities apart from each other.
-
B.
dividedBetween
Indicates that something is partitioned or shared among two or more distinct entities or groups.
-
C.
differentiatedFrom
Indicates that one entity is distinguished or set apart from another by identifying differences between them.
-
D.
discriminatedAgainst
Indicates that one entity treats another unfairly or unequally based on a particular characteristic, such as race, gender, or other protected attributes.
-
E.
divisionTitle
Indicates the formal name or title assigned to a specific division within a larger organization or structure.
- 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_69a2573508588190b522c2476d91acfe |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25c6d0fa08190810139b14f4851bc |
completed | Feb. 28, 2026, 3:09 a.m. |
| PD | Predicate disambiguation | batch_69a25b5357bc8190b29a48e3053fb76d |
completed | Feb. 28, 2026, 3:04 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.