Triple
T7033361
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FLAC |
E163320
|
entity |
| Predicate | supportsLinearPrediction |
P74591
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [FLAC, supportsLinearPrediction, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: supportsLinearPrediction Context triple: [FLAC, supportsLinearPrediction, true]
-
A.
isLinear
Indicates that a relationship, function, or structure preserves linearity, typically meaning it satisfies additivity and homogeneity (or forms a straight-line dependence between variables).
-
B.
hasKeyPrediction
Indicates that one entity contains or provides a primary or most important predicted value or outcome for another entity.
-
C.
hasRegressionAnalysis
Indicates that a regression analysis has been performed on, or is associated with, a given dataset, model, or relationship between variables.
-
D.
linearity
Indicates that a relationship between quantities preserves addition and scalar multiplication, so outputs change in direct proportion to inputs.
-
E.
slopeUse
Indicates how a particular slope or gradient is utilized or purposed in relation to another entity.
- 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_69c6885d691c81908cf7d31083113886 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e458ad9c81908c3f492b317ce291 |
completed | March 27, 2026, 8:11 p.m. |
| PD | Predicate disambiguation | batch_69c6e1b9a2488190aea351d96afa5a12 |
completed | March 27, 2026, 7:59 p.m. |
| PDg | Predicate description generation | batch_69c6e456e89481908df42a1b4232a4a0 |
completed | March 27, 2026, 8:11 p.m. |
Created at: March 27, 2026, 2:36 p.m.