Triple
T13559101
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New York Herald Building |
E323857
|
entity |
| Predicate | mediaTypeHoused |
P110352
|
FINISHED |
| Object | newspaper |
—
|
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: newspaper | Statement: [New York Herald Building, mediaTypeHoused, newspaper]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mediaTypeHoused Context triple: [New York Herald Building, mediaTypeHoused, newspaper]
-
A.
mediaType
Indicates the format or category of media associated with an entity, such as text, image, audio, or video.
-
B.
mediaTypeAvailable
Indicates that a particular type or format of media is available for use, access, or distribution in the given context.
-
C.
ownedMediaType
Indicates the type or category of media content that is owned in the context of the ownership relationship.
-
D.
mediaTypeExample
Indicates that something serves as an example or illustrative instance of a particular media type.
-
E.
mediaTypeOfPresenter
Indicates the type or format of media associated with a given presenter.
- 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_69d8076830b48190910a902bae5888e2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbbb9ee3f081909056dc1a92c40b7a |
completed | April 12, 2026, 3:34 p.m. |
| PD | Predicate disambiguation | batch_69dbae13bec4819084c1770638c00ed9 |
completed | April 12, 2026, 2:37 p.m. |
| PDg | Predicate description generation | batch_69dbbb8c77dc8190b7bd803b5e168d23 |
completed | April 12, 2026, 3:34 p.m. |
Created at: April 9, 2026, 9:47 p.m.