Triple
T924488
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sol |
E19953
|
entity |
| Predicate | coreTemperature_K |
P7283
|
FINISHED |
| Object | approximately 1.5e7 |
—
|
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: approximately 1.5e7 | Statement: [Sol, coreTemperature_K, approximately 1.5e7]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coreTemperature_K Context triple: [Sol, coreTemperature_K, approximately 1.5e7]
-
A.
coreTemperature
chosen
Indicates that an entity has a specific internal or central temperature value.
-
B.
operatingTemperature
Indicates the range or specific value of temperature within which an entity is designed or allowed to function properly.
-
C.
hasTemperature
Indicates that an entity possesses or is characterized by a specific temperature value.
-
D.
maxSurfaceTemperature
Indicates the highest temperature that the surface of an entity can reach or sustain under specified conditions.
-
E.
recordedTemperatureRegion
Indicates that a particular temperature measurement was recorded for or associated with a specific geographic region.
- 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_69a493a099788190a696d9d8408cbaf4 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b3296f50819087f809fbe90b139e |
completed | March 1, 2026, 9:44 p.m. |
| PD | Predicate disambiguation | batch_69a4b295b02481908e5f53bfcb83cc94 |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:40 p.m.