Triple
T135685
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SOFIA airborne observatory operations |
E2740
|
entity |
| Predicate | primaryWavelength |
P5242
|
FINISHED |
| Object | infrared |
—
|
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: infrared | Statement: [SOFIA airborne observatory operations, primaryWavelength, infrared]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryWavelength Context triple: [SOFIA airborne observatory operations, primaryWavelength, infrared]
-
A.
primaryPigment
Indicates that one pigment is the main or dominant colorant used or present in relation to another entity.
-
B.
primaryMode
Indicates the main or most commonly used method, manner, or form in which an action, process, or interaction is carried out between entities.
-
C.
primaryTarget
Indicates that an entity is the main or most important target of another entity’s action, focus, or effect.
-
D.
primaryMotif
Indicates that one entity serves as the main recurring theme or dominant motif associated with another entity.
-
E.
secondaryPigment
Indicates that one pigment functions as a secondary or supporting color relative to another primary pigment in a given context.
- 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_69a2520c0f3481908b0ed054a2fca8d0 |
completed | Feb. 28, 2026, 2:25 a.m. |
| NER | Named-entity recognition | batch_69a257a3ad908190b6a8652f09ae0cbb |
completed | Feb. 28, 2026, 2:49 a.m. |
| PD | Predicate disambiguation | batch_69a25651b9048190a6277b7fec98c1ea |
completed | Feb. 28, 2026, 2:43 a.m. |
| PDg | Predicate description generation | batch_69a256c72f6c81909b619b90d829d86e |
completed | Feb. 28, 2026, 2:45 a.m. |
Created at: Feb. 28, 2026, 2:30 a.m.