Triple
T36374695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moed Katan |
E895866
|
entity |
| Predicate | hasApproximateDafCount |
P139666
|
FINISHED |
| Object | 29 dapim in the Babylonian Talmud |
—
|
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: 29 dapim in the Babylonian Talmud | Statement: [Moed Katan, hasApproximateDafCount, 29 dapim in the Babylonian Talmud]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateDafCount Context triple: [Moed Katan, hasApproximateDafCount, 29 dapim in the Babylonian Talmud]
-
A.
hasNumberOfDaf
chosen
Indicates the specific count of "daf" (pages/folios) associated with an entity.
-
B.
hasApproximateNumberOfResponsa
Indicates that an entity is associated with a rough or estimated count of responsa, rather than an exact number.
-
C.
hasEndingCountApproximate
Indicates that the number of endings associated with an entity is known only approximately rather than as an exact count.
-
D.
maskCountApproximate
Indicates that the number of masks involved is represented as an approximate (non-exact) count.
-
E.
hasApproximateCountInHumans
Indicates that an entity is associated with an estimated or approximate numerical count specifically in humans.
- 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_69f76e5115588190ad8738860b7bc68b |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fe21b0cba48190b56c39e9f1c0eafa |
completed | May 8, 2026, 5:47 p.m. |
| PD | Predicate disambiguation | batch_69fe204576848190aecf204e2adba5dc |
completed | May 8, 2026, 5:41 p.m. |
Created at: May 3, 2026, 4:10 p.m.