Triple
T19772169
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Republic of Cuba (1902–1959) |
E474912
|
entity |
| Predicate | hadSocialIssue |
P55317
|
FINISHED |
| Object | high social inequality |
—
|
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: high social inequality | Statement: [Republic of Cuba (1902–1959), hadSocialIssue, high social inequality]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadSocialIssue Context triple: [Republic of Cuba (1902–1959), hadSocialIssue, high social inequality]
-
A.
socialIssue
Indicates a relationship where something is recognized or treated as a problem or concern affecting society or a community at large.
-
B.
hasSocioeconomicIssue
chosen
Indicates that an entity is affected by, associated with, or involved in a socioeconomic problem or challenge.
-
C.
hadIssue
Indicates that an entity experienced, encountered, or was affected by a particular problem, defect, or difficulty.
-
D.
portraysSocialIssue
Indicates that an entity depicts, represents, or brings attention to a social problem or concern within its content or context.
-
E.
hasUrbanIssue
Indicates that an entity experiences, is affected by, or is associated with a specific problem or challenge related to urban environments or city life.
- 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_69d8e51a43a08190956bc6df13c91a77 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6535ce4d08190a1dfca2df95a8631 |
completed | April 20, 2026, 4:25 p.m. |
| PD | Predicate disambiguation | batch_69e53053ed2881908400becdfada7fd3 |
completed | April 19, 2026, 7:43 p.m. |
Created at: April 10, 2026, 1:48 p.m.