Triple
T2500666
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trump |
E52455
|
entity |
| Predicate | notableInMedia |
P18488
|
FINISHED |
| Object | news coverage |
—
|
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: news coverage | Statement: [Trump, notableInMedia, news coverage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableInMedia Context triple: [Trump, notableInMedia, news coverage]
-
A.
notableMedia
chosen
Indicates a relationship where a media work is recognized as significant, prominent, or especially relevant in connection with a given entity.
-
B.
notableFor
Indicates that an entity is especially recognized or distinguished for a particular quality, achievement, characteristic, or role.
-
C.
notableStory
Indicates that an entity is the subject or source of a story, account, or narrative that is considered notable or significant.
-
D.
notableDuring
Indicates that something was especially prominent, active, or significant during a particular time period or event.
-
E.
notableArticle
Indicates that there exists an article or written work that is especially significant, prominent, or noteworthy in relation to the subject.
- 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_69ab4957b3a88190adf968ae0c1b931c |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd1b144a481909b1f8d96742a92e7 |
completed | March 7, 2026, 7:20 a.m. |
| PD | Predicate disambiguation | batch_69abd0bba5348190bb4637d3165cb339 |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:46 p.m.