Triple
T5905663
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sichuan cuisine |
E131334
|
entity |
| Predicate | typicalSpicinessLevel |
P34515
|
FINISHED |
| Object | high |
—
|
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: high | Statement: [Sichuan cuisine, typicalSpicinessLevel, high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSpicinessLevel Context triple: [Sichuan cuisine, typicalSpicinessLevel, high]
-
A.
hasSpiciness
chosen
Indicates that one entity possesses a certain level or quality of spiciness in relation to another entity or a defined scale.
-
B.
typicalSweetnessLevel
Indicates the usual or characteristic degree of sweetness associated with something.
-
C.
typicalFlavor
Indicates that something characteristically has or is associated with a particular flavor.
-
D.
typicalSauceConsistency
Indicates that something has the usual or characteristic thickness or texture expected of a sauce.
-
E.
hasBitternessLevel
Indicates that an entity is associated with a specific degree or intensity of bitterness.
- 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_69c0085864a88190a569c05ff7d65f29 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03ee10b308190afe38b904ae7c5f7 |
completed | March 22, 2026, 7:11 p.m. |
| PD | Predicate disambiguation | batch_69c0334fcf6481908e8e74105de9d49b |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 3:59 p.m.