Triple
T29768540
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | V399 Carinae |
E753982
|
entity |
| Predicate | surfaceTemperatureCategory |
P100818
|
FINISHED |
| Object | low effective temperature compared to Sun |
—
|
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: low effective temperature compared to Sun | Statement: [V399 Carinae, surfaceTemperatureCategory, low effective temperature compared to Sun]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: surfaceTemperatureCategory Context triple: [V399 Carinae, surfaceTemperatureCategory, low effective temperature compared to Sun]
-
A.
surfaceTemperatureRange
Indicates the range between the minimum and maximum surface temperatures observed or allowed for an entity.
-
B.
surfaceTemperature_K
Indicates the temperature of a surface expressed in kelvins.
-
C.
minSurfaceTemperature
Indicates the lowest temperature value observed or allowed on the surface of an object or environment.
-
D.
hasTemperatureCategory
chosen
Indicates that an entity is associated with a specific qualitative temperature classification (e.g., hot, cold, warm).
-
E.
typicalTemperature
Indicates the usual or characteristic temperature associated with an entity under normal conditions.
- 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_69f0ef827ff88190ade56e0b0846b713 |
completed | April 28, 2026, 5:33 p.m. |
| NER | Named-entity recognition | batch_69f674e06c9481909ed0ea736408f0d7 |
completed | May 2, 2026, 10:04 p.m. |
| PD | Predicate disambiguation | batch_69f673c4abec8190bc2379e66f4af0a9 |
completed | May 2, 2026, 9:59 p.m. |
Created at: April 28, 2026, 8:40 p.m.