Triple
T1236569
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 23 Tauri |
E26561
|
entity |
| Predicate | effectiveTemperature_K |
P4459
|
FINISHED |
| Object | about 14000 |
—
|
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: about 14000 | Statement: [23 Tauri, effectiveTemperature_K, about 14000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectiveTemperature_K Context triple: [23 Tauri, effectiveTemperature_K, about 14000]
-
A.
equilibriumTemperature
Indicates the temperature at which a system’s heat exchange balances so that no net change in its thermal state occurs.
-
B.
typicalTemperature
Indicates the usual or characteristic temperature associated with an entity under normal conditions.
-
C.
hasTemperature
chosen
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.
minSurfaceTemperature
Indicates the lowest temperature value observed or allowed on the surface of an object or environment.
- 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_69a4948571c88190a9191e451e6035fd |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4bf17e0bc8190a066561e6b629fc0 |
completed | March 1, 2026, 10:35 p.m. |
| PD | Predicate disambiguation | batch_69a4bb67d52c8190815d6356b79d6ed5 |
completed | March 1, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:47 p.m.