Triple
T17950925
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Panchadasi |
E448828
|
entity |
| Predicate | numberOfChaptersInEachSection |
P2946
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [Panchadasi, numberOfChaptersInEachSection, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfChaptersInEachSection Context triple: [Panchadasi, numberOfChaptersInEachSection, 5]
-
A.
numberOfChapters
chosen
Indicates the total count of chapters associated with a given entity.
-
B.
scripturalChapters
Indicates that one entity is composed of, contains, or is divided into the specified scriptural chapters.
-
C.
chapterNumber
Indicates the specific ordinal position a chapter occupies within a larger ordered work, such as a book or document.
-
D.
hasChapterStructure
Indicates that one entity is organized into chapters or contains a defined chapter-based structure in relation to another entity.
-
E.
containsChapter
Indicates that one entity (typically a larger work or document) includes another entity as a chapter within its structure.
- 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_69d8b9f8cca8819099836916c56b7c95 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e4afac95048190a5d1ef012899c62b |
completed | April 19, 2026, 10:34 a.m. |
| PD | Predicate disambiguation | batch_69e3f8f2bd088190b1e22ad4d9cc8b13 |
completed | April 18, 2026, 9:34 p.m. |
Created at: April 10, 2026, 10:21 a.m.