Triple
T6293622
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bernoulli trials |
E141077
|
entity |
| Predicate | hasOutcomeSpacePerTrial |
P70747
|
FINISHED |
| Object | {0,1} |
—
|
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: {0,1} | Statement: [Bernoulli trials, hasOutcomeSpacePerTrial, {0,1}]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOutcomeSpacePerTrial
Context triple: [Bernoulli trials, hasOutcomeSpacePerTrial, {0,1}]
-
A.
outcomeOfTrial
Indicates that a particular result or verdict is produced as the consequence of a specific trial or legal proceeding.
-
B.
hasEventOutcome
Indicates that a particular event results in, leads to, or is associated with a specific outcome.
-
C.
hasNumberOfTrails
Indicates the specific count of trails associated with or available in a given entity.
-
D.
hasFirstTrial
Indicates that an entity is associated with its initial or earliest trial, test, or legal proceeding.
-
E.
hasTypeOfExperiment
Indicates that an experiment is associated with or classified under a specific type or category of experiment.
- 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_69c008cdf2ac8190bb640c94478fb4ed |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c06438654481908c9833c5f0d61773 |
completed | March 22, 2026, 9:50 p.m. |
| PD | Predicate disambiguation | batch_69c060df0d8881908215575862ef6831 |
completed | March 22, 2026, 9:36 p.m. |
| PDg | Predicate description generation | batch_69c06284848c8190a0151ff3e8682889 |
completed | March 22, 2026, 9:43 p.m. |
Created at: March 22, 2026, 4:27 p.m.