Triple
T15886783
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | dayan |
E385210
|
entity |
| Predicate | minimumNumberOnCourt |
P120936
|
FINISHED |
| Object | three dayanim for standard beit din |
—
|
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: three dayanim for standard beit din | Statement: [dayan, minimumNumberOnCourt, three dayanim for standard beit din]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: minimumNumberOnCourt Context triple: [dayan, minimumNumberOnCourt, three dayanim for standard beit din]
-
A.
minimumFemalePlayersOnField
Indicates the rule that specifies the least number of female players that must be present on the field at any given time.
-
B.
playerNumber
Indicates the specific jersey or identification number assigned to a player within a team or game context.
-
C.
numberOfPlayersPerTeam
Indicates the quantity of players that are assigned to or allowed on each team in a given context.
-
D.
usesNumberOfPlayersOnFieldPerTeam
Indicates that the relationship specifies or depends on how many players each team has on the field at a given time.
-
E.
playersPerSideOnField
Indicates the number of players from each team that are simultaneously present on the field during play.
- F. None of above. chosen
Provenance (4 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_69d86da5b800819083a31be937d738b0 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e174de2cd48190ab18e48c9f051a2a |
completed | April 16, 2026, 11:46 p.m. |
| PD | Predicate disambiguation | batch_69e142c3e18c8190bb7b023f4a0eaebb |
completed | April 16, 2026, 8:12 p.m. |
| PDg | Predicate description generation | batch_69e174da2c2c819099ec46616798245a |
completed | April 16, 2026, 11:46 p.m. |
Created at: April 10, 2026, 4:51 a.m.