Triple
T9644548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Phoebe Pyncheon |
E233159
|
entity |
| Predicate | appearsInChapterCountApprox |
P795
|
FINISHED |
| Object | multiple chapters throughout the novel |
—
|
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: multiple chapters throughout the novel | Statement: [Phoebe Pyncheon, appearsInChapterCountApprox, multiple chapters throughout the novel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appearsInChapterCountApprox Context triple: [Phoebe Pyncheon, appearsInChapterCountApprox, multiple chapters throughout the novel]
-
A.
numberOfChapters
Indicates the total count of chapters associated with a given entity.
-
B.
containsChapter
Indicates that one entity (typically a larger work or document) includes another entity as a chapter within its structure.
-
C.
chapterNumber
Indicates the specific ordinal position a chapter occupies within a larger ordered work, such as a book or document.
-
D.
gameChapter
Indicates that one entity is a chapter, level, or segment that forms part of the progression or structure of a game.
-
E.
appearsIn
chosen
Indicates that an entity is present, featured, or occurs within a particular context, work, or medium.
- 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_69ca848a5a908190aad251f4137b0c3a |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9b7fd2308190803a196ecdc80d76 |
completed | April 1, 2026, 10:26 p.m. |
| PD | Predicate disambiguation | batch_69ccd5b0263081908cf6df3eb07d71b0 |
completed | April 1, 2026, 8:22 a.m. |
Created at: March 30, 2026, 8:12 p.m.