Triple
T9787554
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Biography of the Pixel |
E237522
|
entity |
| Predicate | hasChapterTopic |
P32494
|
FINISHED |
| Object | sampling theory |
—
|
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: sampling theory | Statement: [A Biography of the Pixel, hasChapterTopic, sampling theory]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasChapterTopic Context triple: [A Biography of the Pixel, hasChapterTopic, sampling theory]
-
A.
containsChapter
Indicates that one entity (typically a larger work or document) includes another entity as a chapter within its structure.
-
B.
chapterOn
chosen
Indicates that one entity (typically a chapter) is about, discusses, or focuses on the subject represented by another entity.
-
C.
containsSubchapter
Indicates that one chapter or section includes another, more specific subchapter as a part of its structure.
-
D.
hadChapterOf
Indicates that an entity (such as a book or document) includes or contains a specific chapter as one of its parts.
-
E.
hasLocalChaptersIn
Indicates that an organization maintains one or more local chapters or branches within a specified geographic area or location.
- 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_69ca84da927881909bda80caecad6010 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda211b0608190bc8ceb905d02db83 |
completed | April 1, 2026, 10:54 p.m. |
| PD | Predicate disambiguation | batch_69cd03d77c6c81909b675955bf113320 |
completed | April 1, 2026, 11:39 a.m. |
Created at: March 30, 2026, 8:27 p.m.