Triple
T25327075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shrikhand |
E635045
|
entity |
| Predicate | cardamomEffect |
P35696
|
FINISHED |
| Object | adds warm, aromatic flavor |
—
|
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: adds warm, aromatic flavor | Statement: [Shrikhand, cardamomEffect, adds warm, aromatic flavor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cardamomEffect Context triple: [Shrikhand, cardamomEffect, adds warm, aromatic flavor]
-
A.
foodEffect
Indicates how consuming a particular food influences or changes another entity, such as an organism, condition, or process.
-
B.
typicalSpices
Indicates that certain spices are commonly or characteristically used in association with a particular dish, cuisine, or ingredient.
-
C.
effectOnCheese
Indicates the influence or impact that one entity or action has on the state, quality, or characteristics of cheese.
-
D.
providesSensoryEffects
chosen
Indicates that one entity causes or contributes to sensory experiences or perceptions in another entity.
-
E.
hasPharmacologicalEffect
Indicates that one entity produces a specific pharmacological effect or action on another entity.
- 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_69e75a9908108190a95427a97020632a |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f497bf12e081908b8c0f523586fca3 |
completed | May 1, 2026, 12:08 p.m. |
| PD | Predicate disambiguation | batch_69f45d06d0388190b36ecde92013624a |
completed | May 1, 2026, 7:57 a.m. |
Created at: April 21, 2026, 1:30 p.m.