Triple
T9607365
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Turning Pages: My Life Story |
E232004
|
entity |
| Predicate | libraryOfCongressCategory |
P33059
|
FINISHED |
| Object | juvenile literature |
—
|
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: juvenile literature | Statement: [Turning Pages: My Life Story, libraryOfCongressCategory, juvenile literature]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: libraryOfCongressCategory Context triple: [Turning Pages: My Life Story, libraryOfCongressCategory, juvenile literature]
-
A.
bookCategory
Indicates the classification or genre category to which a given book belongs.
-
B.
libraryOfCongressSubjectHeading
chosen
Indicates that one entity is a Library of Congress Subject Heading used as the controlled subject term for describing or indexing the other entity.
-
C.
libraryOfCongressClassification
Indicates that one entity is assigned a Library of Congress Classification code that organizes it within the Library of Congress subject-based cataloging system.
-
D.
genreDocumented
Indicates that a work’s genre has been formally recorded or documented.
-
E.
textCategory
Indicates that a piece of text belongs to or is classified under a particular category or type.
- 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_69ca8485a90c819094fe40b42fde9d70 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9a62372881908bf21be91e7285fb |
completed | April 1, 2026, 10:21 p.m. |
| PD | Predicate disambiguation | batch_69ccd5a6fd2481908efd131e207b8143 |
completed | April 1, 2026, 8:21 a.m. |
Created at: March 30, 2026, 8:08 p.m.