Triple
T3839735
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1036 Ganymed |
E93420
|
entity |
| Predicate | hasLightcurveMeasurements |
P52321
|
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: [1036 Ganymed, hasLightcurveMeasurements, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLightcurveMeasurements Context triple: [1036 Ganymed, hasLightcurveMeasurements, true]
-
A.
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.
-
B.
hasLightcurveAmplitude
Indicates the measured range of brightness variation (amplitude) in an object's lightcurve over time.
-
C.
hasTransitLightCurve
Indicates that an object exhibits a measurable transit light curve, showing periodic dips in observed brightness due to another body passing in front of it.
-
D.
lightcurveInterpretation
Indicates the inferred physical explanation or model derived from analyzing an object's observed light curve behavior.
-
E.
lightcurveAmplitude
Indicates the measured range of brightness variation of an object over time in its light curve.
- 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_69aed96ce578819084ab16e3439976c9 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69aeeba1535c8190b36e2ab2d4514b54 |
completed | March 9, 2026, 3:47 p.m. |
| PD | Predicate disambiguation | batch_69aee74dcecc819098285483ec721b40 |
completed | March 9, 2026, 3:29 p.m. |
| PDg | Predicate description generation | batch_69aeeb828fb08190901d51edbe8bd304 |
completed | March 9, 2026, 3:47 p.m. |
Created at: March 9, 2026, 3:18 p.m.