Triple
T8564688
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dosa |
E202774
|
entity |
| Predicate | isUsuallyServedHotOrCold |
P66977
|
FINISHED |
| Object | hot |
—
|
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: hot | Statement: [Dosa, isUsuallyServedHotOrCold, hot]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isUsuallyServedHotOrCold Context triple: [Dosa, isUsuallyServedHotOrCold, hot]
-
A.
servedHot
Indicates that something is provided or presented in a heated or warm state, suitable for immediate consumption.
-
B.
recommendedServingTemperature
chosen
Indicates the temperature at which something (typically food or drink) is advised to be served for optimal use or enjoyment.
-
C.
servingStyle
Indicates how something (typically food or drink) is presented or offered for consumption or use.
-
D.
requiresCookingTemperature
Indicates that performing the action or preparing the item necessitates reaching or maintaining a specific cooking temperature.
-
E.
isTypicallyGarnishedWith
Indicates that one item is commonly used as a garnish or decorative finishing element for another.
- 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_69ca8327b0a881908606ff860713964d |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe9d11274819099cc33a21a993a1f |
completed | March 31, 2026, 3:35 p.m. |
| PD | Predicate disambiguation | batch_69cbd11856048190a1ce4b83a38f6965 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:20 p.m.