Triple
T15951939
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TGV V150 |
E386837
|
entity |
| Predicate | testSectionGradient |
P69426
|
FINISHED |
| Object | slight downhill gradient used |
—
|
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: slight downhill gradient used | Statement: [TGV V150, testSectionGradient, slight downhill gradient used]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: testSectionGradient Context triple: [TGV V150, testSectionGradient, slight downhill gradient used]
-
A.
hasGradientSection
chosen
Indicates that an entity includes a portion or segment where some property changes gradually rather than remaining constant.
-
B.
hasGradient
Indicates that one entity possesses or is characterized by a gradual change in value, intensity, or property across its extent or between two points.
-
C.
usesGradientInformation
Indicates that an entity performs its operation by leveraging gradient (derivative) information, typically to guide optimization or learning steps.
-
D.
averageGradient
Indicates the mean rate of change (slope) of a quantity over a specified interval or region.
-
E.
temperatureGradient
Indicates how temperature changes in magnitude and/or direction across space within a given region or system.
- 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_69d86da882448190a82ea962fe343b79 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e17d4d08f481909f38b75e3f42d9ab |
completed | April 17, 2026, 12:22 a.m. |
| PD | Predicate disambiguation | batch_69e142d37cd88190ab50760f1783e20c |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:53 a.m.