Triple
T29403264
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Golden Age of Hollywood serials |
E745709
|
entity |
| Predicate | typicalChapterCount |
P2946
|
FINISHED |
| Object | 12 to 15 chapters |
—
|
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: 12 to 15 chapters | Statement: [Golden Age of Hollywood serials, typicalChapterCount, 12 to 15 chapters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalChapterCount Context triple: [Golden Age of Hollywood serials, typicalChapterCount, 12 to 15 chapters]
-
A.
numberOfChapters
chosen
Indicates the total count of chapters associated with a given entity.
-
B.
chapterLength
Indicates the length or extent of a chapter, typically measured in units such as pages, words, or time.
-
C.
runtimePerChapterApprox
Indicates an approximate amount of time required to complete or experience each chapter.
-
D.
containsChapter
Indicates that one entity (typically a larger work or document) includes another entity as a chapter within its structure.
-
E.
chapterNumber
Indicates the specific ordinal position a chapter occupies within a larger ordered work, such as a book or document.
- 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_69f0a79eb7d081908c67197a5f347e68 |
completed | April 28, 2026, 12:27 p.m. |
| NER | Named-entity recognition | batch_6a01b50490e481908eb1d675561fdde2 |
completed | May 11, 2026, 10:52 a.m. |
| PD | Predicate disambiguation | batch_6a01b44b106481908aa98a3f1eb2c119 |
completed | May 11, 2026, 10:49 a.m. |
Created at: April 28, 2026, 2:52 p.m.