Triple
T20187956
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fahrenheit |
E492910
|
entity |
| Predicate | maximumDropAngle |
P23303
|
FINISHED |
| Object | 97 degrees |
—
|
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: 97 degrees | Statement: [Fahrenheit, maximumDropAngle, 97 degrees]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maximumDropAngle Context triple: [Fahrenheit, maximumDropAngle, 97 degrees]
-
A.
dropAngle
Indicates the angle at which something is dropped or released relative to a reference direction or surface.
-
B.
maxVerticalAngle
chosen
Indicates the greatest vertical angular difference or tilt between two entities or directions.
-
C.
firstDropAngle
Indicates the angle at which an object or entity initially begins to fall or be dropped from its starting position.
-
D.
lowerInclinationAngle
Indicates that one entity has a smaller or more downward-tilted inclination angle compared to another entity.
-
E.
maximumGradient
Indicates the greatest rate of change or steepest slope that occurs within a given function, surface, or dataset.
- 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_69da6268a034819081cbd9ea5a1c9475 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66ad2c43c8190a2fc5ef2a0514e53 |
completed | April 20, 2026, 6:05 p.m. |
| PD | Predicate disambiguation | batch_69e55b11124c8190babacf2a0fe2d057 |
completed | April 19, 2026, 10:45 p.m. |
Created at: April 11, 2026, 11:37 p.m.