Triple
T18776161
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | R Scuti |
E459138
|
entity |
| Predicate | lightCurveShape |
P122576
|
FINISHED |
| Object | alternating deep and shallow minima |
—
|
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: alternating deep and shallow minima | Statement: [R Scuti, lightCurveShape, alternating deep and shallow minima]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lightCurveShape Context triple: [R Scuti, lightCurveShape, alternating deep and shallow minima]
-
A.
lightCurveBehavior
chosen
Indicates how the intensity or brightness of an object changes over time, typically as represented in its light curve.
-
B.
lightcurveIndicates
Indicates that the characteristics or pattern of an object's light curve provide evidence for or reveal information about a particular property, state, or event associated with that object.
-
C.
lightcurveInterpretation
Indicates the inferred physical explanation or model derived from analyzing an object's observed light curve behavior.
-
D.
lightcurveAmplitude
Indicates the measured range of brightness variation of an object over time in its light curve.
-
E.
hasLightcurveVariations
Indicates that an object exhibits measurable changes in its brightness over time, as captured in its lightcurve.
- 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_69d8d396f54c8190ba49db31e8743842 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5933b912481908bfd97216eacb257 |
completed | April 20, 2026, 2:45 a.m. |
| PD | Predicate disambiguation | batch_69e48d1126e4819099607837ed5aadca |
completed | April 19, 2026, 8:06 a.m. |
Created at: April 10, 2026, 11:52 a.m.