Triple
T32395
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manufacturing Consent: The Political Economy of the Mass Media |
E646
|
entity |
| Predicate | hasCaseStudy |
P2398
|
FINISHED |
| Object | coverage of the Vietnam War |
—
|
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: coverage of the Vietnam War | Statement: [Manufacturing Consent: The Political Economy of the Mass Media, hasCaseStudy, coverage of the Vietnam War]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCaseStudy Context triple: [Manufacturing Consent: The Political Economy of the Mass Media, hasCaseStudy, coverage of the Vietnam War]
-
A.
hasExample
Indicates that one entity serves as an instance, illustration, or concrete example of another entity.
-
B.
hasNumberOfCasesApprox
Indicates that an entity is associated with an approximate (not exact) count of cases.
-
C.
hasPublication
Indicates that an entity is associated with or responsible for a specific publication.
-
D.
hasResearchArea
Indicates that an entity (such as a person, project, or organization) is associated with or focused on a particular field or area of research.
-
E.
isStudiedIn
Indicates that a subject (such as a topic, field, or phenomenon) is examined, researched, or learned about within a particular context, environment, or discipline.
- F. None of above. chosen
Provenance (4 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_69a2479dec388190967ba648663442c9 |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a249ec0d288190ac3a0939db61813b |
completed | Feb. 28, 2026, 1:50 a.m. |
| PD | Predicate disambiguation | batch_69a24870417081909c7c01e400c94716 |
completed | Feb. 28, 2026, 1:44 a.m. |
| PDg | Predicate description generation | batch_69a249eb52a08190916849b44bd9d68d |
completed | Feb. 28, 2026, 1:50 a.m. |
Created at: Feb. 28, 2026, 1:44 a.m.