Triple
T24810995
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mueller calculus |
E620786
|
entity |
| Predicate | outputQuantity |
P157358
|
FINISHED |
| Object | Stokes vector |
—
|
NE NERFINISHED |
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: Stokes vector | Statement: [Mueller calculus, outputQuantity, Stokes vector]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: outputQuantity Context triple: [Mueller calculus, outputQuantity, Stokes vector]
-
A.
orderQuantity
Indicates the specific amount or number of items requested or scheduled in an order transaction.
-
B.
mainQuantity
Indicates that the associated value represents the primary or principal quantity in a given context or relationship.
-
C.
usesQuantity
Indicates that one entity employs or applies a specified amount or measure of another entity in performing an action or fulfilling a function.
-
D.
productionCapacity
Indicates the maximum amount of output an entity can produce within a given time or resource constraint.
-
E.
numberOfUnits
Indicates the quantity or count of discrete units associated with an entity or relationship.
- 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_69e2fabf26bc8190b191faac8f67065b |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f42d9000b8819081ea2605f3c193d6 |
completed | May 1, 2026, 4:35 a.m. |
| PD | Predicate disambiguation | batch_69f420f471a0819095a6cd24ed8f7476 |
completed | May 1, 2026, 3:41 a.m. |
| PDg | Predicate description generation | batch_69f42b11251881908070b93355de64ad |
completed | May 1, 2026, 4:24 a.m. |
Created at: April 18, 2026, 4:50 a.m.