Triple
T6939213
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laplace law of error |
E160629
|
entity |
| Predicate | skewness |
P27170
|
FINISHED |
| Object | 0 |
—
|
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 | Statement: [Laplace law of error, skewness, 0]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: skewness Context triple: [Laplace law of error, skewness, 0]
-
A.
hasSkewness
chosen
Indicates that a distribution or dataset exhibits a specific degree and direction of asymmetry around its central value.
-
B.
arity
Indicates the number of arguments or participants that a relation or function takes.
-
C.
bias
Indicates a systematic preference or prejudice in favor of or against an entity, affecting how it is treated, evaluated, or represented relative to others.
-
D.
asymmetric
Indicates that the relationship between two entities never holds in both directions simultaneously, so if it holds from A to B it cannot also hold from B to A.
-
E.
parity
Indicates that two quantities share the same evenness or oddness, or more generally that they have equivalent status or value in a given context.
- 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_69c6884f3db4819080ad65da69386206 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e0c74fe48190aeaa018631e52ef6 |
completed | March 27, 2026, 7:55 p.m. |
| PD | Predicate disambiguation | batch_69c6d7bd5a388190a57a96d925696ff6 |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:28 p.m.