Triple
T31220764
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scoville heat units |
E796002
|
entity |
| Predicate | hasUnitQuantityKind |
P73891
|
FINISHED |
| Object | pungency |
—
|
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: pungency | Statement: [Scoville heat units, hasUnitQuantityKind, pungency]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUnitQuantityKind Context triple: [Scoville heat units, hasUnitQuantityKind, pungency]
-
A.
hasUnitOf
Indicates that a quantity, measurement, or value is expressed in terms of a specific unit.
-
B.
hasBaseQuantity
Indicates that something is associated with or defined in terms of a fundamental underlying quantity.
-
C.
belongsToQuantityKind
chosen
Indicates that something is associated with, or classified under, a particular kind or category of quantity (such as length, mass, or time).
-
D.
hasUnitIn
Indicates that one entity is contained or measured within another as a unit, specifying a unit-of-measure or component relationship.
-
E.
isAUnitOf
Indicates that one entity functions as a unit or standard measure in which the other entity is quantified or expressed.
- 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_69f224d9d52c8190a61f68ded37fa755 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f7805ce6208190ac6dbd9c97989978 |
completed | May 3, 2026, 5:05 p.m. |
| PD | Predicate disambiguation | batch_69f77956ec648190ba4fb7e9d83fd107 |
completed | May 3, 2026, 4:35 p.m. |
Created at: April 29, 2026, 9:10 p.m.