Triple
T36467637
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wigner semicircle law |
E898462
|
entity |
| Predicate | hasDensityShape |
P74406
|
FINISHED |
| Object | semicircle |
—
|
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: semicircle | Statement: [Wigner semicircle law, hasDensityShape, semicircle]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDensityShape Context triple: [Wigner semicircle law, hasDensityShape, semicircle]
-
A.
hasDensityParameter
Indicates that an entity is associated with a specific density-related parameter or value used to characterize its density properties.
-
B.
hasMeanDensity
Indicates that one entity possesses a specified average mass per unit volume (mean density).
-
C.
hasSurfaceDensity
Indicates that one entity possesses or is characterized by a specific amount of mass or quantity distributed per unit area on its surface.
-
D.
hasProbabilityDensityShape
chosen
Indicates that one entity exhibits, is characterized by, or is associated with a particular shape or form of a probability density function.
-
E.
typeOfDensity
Indicates the specific category or kind of density (e.g., mass, population, charge) that characterizes a given quantity or measurement.
- 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_69f76e58ebd88190b75d9b169b59d793 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69ff6fba1a5c8190a660279a6271d785 |
completed | May 9, 2026, 5:32 p.m. |
| PD | Predicate disambiguation | batch_69ff6f59388c8190a7d6ab7bc7705bc0 |
completed | May 9, 2026, 5:31 p.m. |
Created at: May 3, 2026, 4:10 p.m.