Triple
T2234945
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ligeia Mare |
E49257
|
entity |
| Predicate | imagedIn |
P37258
|
FINISHED |
| Object | radar wavelengths |
—
|
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: radar wavelengths | Statement: [Ligeia Mare, imagedIn, radar wavelengths]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: imagedIn Context triple: [Ligeia Mare, imagedIn, radar wavelengths]
-
A.
capturedIn
Indicates that one entity was taken prisoner, seized, or otherwise brought under control within the context, location, or event represented by another entity.
-
B.
usesImageryOf
Indicates that one entity employs or incorporates visual or sensory imagery that depicts, references, or symbolically represents another entity.
-
C.
hasPhotograph
Indicates that one entity possesses, includes, or is associated with a photograph depicting or representing another entity.
-
D.
publicImage
Indicates how an entity is perceived or represented by the general public or broader audience.
-
E.
inverseImage
Indicates the mapping from a set of outputs back to all inputs that are related to those outputs under a given function or relation.
- 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_69a88aa84bdc819086df50e9c20b301e |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc09297a481909e8fe6ec645de616 |
completed | March 7, 2026, 6:07 a.m. |
| PD | Predicate disambiguation | batch_69abbdafc07881909101266a33ae7031 |
completed | March 7, 2026, 5:54 a.m. |
| PDg | Predicate description generation | batch_69abbf9c77fc8190a323bcaf644fb2c5 |
completed | March 7, 2026, 6:03 a.m. |
Created at: March 4, 2026, 7:47 p.m.