Triple
T6397100
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Unruh effect |
E143967
|
entity |
| Predicate | temperatureSymbol |
P29590
|
FINISHED |
| Object | T |
—
|
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: T | Statement: [Unruh effect, temperatureSymbol, T]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: temperatureSymbol Context triple: [Unruh effect, temperatureSymbol, T]
-
A.
symbolOfTemperature
chosen
Indicates that one entity serves as a symbolic representation or notation used to express the temperature of another entity.
-
B.
typicalTemperatureScale
Indicates the temperature scale (such as Celsius or Fahrenheit) that is normally used to express temperature values for the given entity or context.
-
C.
hasTemperature
Indicates that an entity possesses or is characterized by a specific temperature value.
-
D.
temperatureProportionalTo
Indicates that the temperature of one entity changes in direct proportion to the temperature of another entity.
-
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_69c008db906c819096f3597d55d95432 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c06896d180819091548a728e903184 |
completed | March 22, 2026, 10:09 p.m. |
| PD | Predicate disambiguation | batch_69c060f25c088190b433f78553ff1d84 |
completed | March 22, 2026, 9:36 p.m. |
Created at: March 22, 2026, 4:35 p.m.