Triple
T26364820
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sayur asem Betawi |
E660302
|
entity |
| Predicate | liquidComponent |
P100253
|
FINISHED |
| Object | water flavored with tamarind |
—
|
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: water flavored with tamarind | Statement: [Sayur asem Betawi, liquidComponent, water flavored with tamarind]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: liquidComponent Context triple: [Sayur asem Betawi, liquidComponent, water flavored with tamarind]
-
A.
densityLiquid
Indicates that the predicate specifies the mass per unit volume of a substance when it is in its liquid state.
-
B.
concentrationComponent
Indicates that one entity is a constituent or ingredient whose amount contributes to the overall concentration of another entity (such as a mixture, solution, or sample).
-
C.
hasFluid
chosen
Indicates that one entity contains, holds, or is associated with a particular fluid.
-
D.
formulatedIn
Indicates that something was created, developed, or expressed within a particular context, place, or framework.
-
E.
perfectFluidForm
Indicates that an entity assumes or exhibits the properties and behavior of a perfect (ideal) fluid in relation to another entity or context.
- 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_69ee8126d52c8190bc0b34337c2c9aa8 |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69f6102b07fc81908c14c1bcca28c1b0 |
completed | May 2, 2026, 2:54 p.m. |
| PD | Predicate disambiguation | batch_69f5f800fa9c8190aab0962669fde8ac |
completed | May 2, 2026, 1:11 p.m. |
Created at: April 26, 2026, 10:54 p.m.