Triple
T37730303
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Velvet |
E940134
|
entity |
| Predicate | pageCountType |
P188980
|
FINISHED |
| Object | standard comic book length |
—
|
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: standard comic book length | Statement: [Velvet, pageCountType, standard comic book length]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pageCountType Context triple: [Velvet, pageCountType, standard comic book length]
-
A.
hasPageCountApprox
Indicates that an entity is associated with an approximate or estimated number of pages, rather than an exact page count.
-
B.
pageSize
Indicates the size or amount of content (such as items, records, or data) that is included or displayed on a single page.
-
C.
codePageNumber
Indicates the specific page number within a code document or code listing where something is located or referenced.
-
D.
pageNumber
Indicates the specific numerical position of a page within an ordered sequence of pages.
-
E.
pageCountUSHardcover
Indicates the number of pages in the U.S. hardcover edition of a work.
- 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_69f76edefd048190a32212c5c3919531 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fbaf48ba148190a8bdfd6846ad0540 |
completed | May 6, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69fbadf632ec8190b14991c971258307 |
completed | May 6, 2026, 9:09 p.m. |
| PDg | Predicate description generation | batch_69fbaeef9c488190babb546b962b4fb6 |
completed | May 6, 2026, 9:13 p.m. |
Created at: May 3, 2026, 4:18 p.m.