Triple
T492743
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CNN |
E10223
|
entity |
| Predicate | coverageFocus |
P31
|
FINISHED |
| Object | breaking news |
—
|
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: breaking news | Statement: [CNN, coverageFocus, breaking news]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coverageFocus Context triple: [CNN, coverageFocus, breaking news]
-
A.
focusesOn
chosen
Indicates that one entity directs its attention, effort, or primary activity toward another entity or specific subject.
-
B.
missionFocus
Indicates that an entity’s primary attention, effort, or resources are directed toward a particular mission, goal, or objective.
-
C.
focusPeriod
Indicates the specific time span during which attention, activity, or analysis is concentrated on something.
-
D.
coversPolicyArea
Indicates that a policy, document, or initiative includes or addresses a particular policy area or topic within its scope.
-
E.
primaryTopicOf
Indicates that a given subject is the main or central topic described by another resource (such as a document, page, or record).
- 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_69a2e847df8481909239ec08ccf1e376 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f0faab4881909f65f172198b5bd2 |
completed | Feb. 28, 2026, 1:43 p.m. |
| PD | Predicate disambiguation | batch_69a2edf7ce008190836fb6ab5ea39375 |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.