Triple
T13575138
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tractate Beitzah |
E324261
|
entity |
| Predicate | hasNumberOfChaptersInMishnah |
P98049
|
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: [Tractate Beitzah, hasNumberOfChaptersInMishnah, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfChaptersInMishnah Context triple: [Tractate Beitzah, hasNumberOfChaptersInMishnah, 5]
-
A.
hasMishnahChapters
chosen
Indicates that an entity (typically a tractate or section) is associated with a specific number or set of chapters in the Mishnah.
-
B.
numberOfTractates
Indicates the total count of tractates associated with a given entity or collection.
-
C.
numberOfChapters
Indicates the total count of chapters associated with a given entity.
-
D.
containsChapter
Indicates that one entity (typically a larger work or document) includes another entity as a chapter within its structure.
-
E.
scripturalChapters
Indicates that one entity is composed of, contains, or is divided into the specified scriptural chapters.
- 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_69d80769100c819099111274614f5ed2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb02b1f108190a12af382d1de70bb |
completed | April 12, 2026, 2:46 p.m. |
| PD | Predicate disambiguation | batch_69dbae161a0481909f9d3f40ca4e0ac5 |
completed | April 12, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:48 p.m.