Triple
T7199552
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sabine reverberation formula |
E168704
|
entity |
| Predicate | mainQuantity |
P75768
|
FINISHED |
| Object | reverberation time |
—
|
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: reverberation time | Statement: [Sabine reverberation formula, mainQuantity, reverberation time]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainQuantity Context triple: [Sabine reverberation formula, mainQuantity, reverberation time]
-
A.
usesQuantity
Indicates that one entity employs or applies a specified amount or measure of another entity in performing an action or fulfilling a function.
-
B.
orderQuantity
Indicates the specific amount or number of items requested or scheduled in an order transaction.
-
C.
quantityType
Indicates that one entity is the type or category of quantity to which another entity (a specific measured or measurable amount) belongs.
-
D.
quantificationType
Indicates the specific kind or category of quantity or measurement being applied in a given context.
-
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_69c68a5376748190bb500f03df86e93e |
completed | March 27, 2026, 1:46 p.m. |
| NER | Named-entity recognition | batch_69c6e92b8bc08190bfcdd34ce42e3448 |
completed | March 27, 2026, 8:31 p.m. |
| PD | Predicate disambiguation | batch_69c6e757fed4819091b0a096e3befc3a |
completed | March 27, 2026, 8:23 p.m. |
| PDg | Predicate description generation | batch_69c6e8fd9b848190b2b1beea5698422b |
completed | March 27, 2026, 8:30 p.m. |
Created at: March 27, 2026, 2:52 p.m.