Triple
T14910972
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tukey's lambda distribution |
E371258
|
entity |
| Predicate | hasShapeParameter |
P4452
|
FINISHED |
| Object | lambda |
—
|
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: lambda | Statement: [Tukey's lambda distribution, hasShapeParameter, lambda]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasShapeParameter Context triple: [Tukey's lambda distribution, hasShapeParameter, lambda]
-
A.
hasParameterization
Indicates that one entity serves as the specific parameterization or parameter-setting scheme used to define or configure another entity.
-
B.
hasShapeModel
Indicates that an entity is associated with a specific geometric or structural shape model that represents its form.
-
C.
hasTerminalShape
Indicates that one entity possesses or exhibits a particular terminal (end) shape defined by another entity.
-
D.
hasDimensionParameter
Indicates that an entity is associated with a specific dimensional parameter (such as size, length, width, or height) that characterizes its measurable extent.
-
E.
hasParameter
chosen
Indicates that an entity is associated with a specific parameter that defines or constrains some aspect of its behavior, configuration, or characteristics.
- 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_69d85cc7ea3481908228b5acb7d06f12 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded61c6b9c8190a92934d49b98fe46 |
completed | April 15, 2026, 12:04 a.m. |
| PD | Predicate disambiguation | batch_69de9a4a14a88190951bb8f4c60bd37b |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:26 a.m.