Triple
T2234976
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ligeia Mare |
E49257
|
entity |
| Predicate | temperatureEstimate |
P4459
|
FINISHED |
| Object | around 90 to 95 Kelvin |
—
|
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: around 90 to 95 Kelvin | Statement: [Ligeia Mare, temperatureEstimate, around 90 to 95 Kelvin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: temperatureEstimate Context triple: [Ligeia Mare, temperatureEstimate, around 90 to 95 Kelvin]
-
A.
typicalTemperature
Indicates the usual or characteristic temperature associated with an entity under normal conditions.
-
B.
hasTemperature
chosen
Indicates that an entity possesses or is characterized by a specific temperature value.
-
C.
averageTemperature
Indicates the typical or mean temperature value associated with an entity over a specified period or context.
-
D.
temperatureProportionalTo
Indicates that the temperature of one entity changes in direct proportion to the temperature of another entity.
-
E.
weatherCondition
Indicates the type of atmospheric state or weather pattern (e.g., sunny, rainy, snowy) affecting a location or time period.
- 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_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. |
Created at: March 4, 2026, 7:47 p.m.