Triple
T15247479
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Detective Story Magazine |
E364421
|
entity |
| Predicate | pageCountRange |
P9415
|
FINISHED |
| Object | approximately 144–160 pages per issue |
—
|
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: approximately 144–160 pages per issue | Statement: [Detective Story Magazine, pageCountRange, approximately 144–160 pages per issue]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pageCountRange Context triple: [Detective Story Magazine, pageCountRange, approximately 144–160 pages per issue]
-
A.
hasPageCountApprox
chosen
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.
bookChapterRange
Indicates a relationship where a specified range of chapters within a book is identified or referenced as a contiguous segment.
-
D.
hasPageRangeInMushaf
Indicates that a text segment spans a specific range of pages within a particular mushaf (written copy of the Quran).
-
E.
linesPerPage
Indicates the number of lines that are contained or displayed on a single page.
- 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_69d85a0dde7481908fc64d1e82d5d20d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e007f4f9d48190b96a7e0c6993cd69 |
completed | April 15, 2026, 9:49 p.m. |
| PD | Predicate disambiguation | batch_69deca899d5c8190be4a7c71e1683c69 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:13 a.m.