Triple
T30959978
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kare-Kare |
E788783
|
entity |
| Predicate | usesThickener |
P84495
|
FINISHED |
| Object | ground roasted peanuts |
—
|
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: ground roasted peanuts | Statement: [Kare-Kare, usesThickener, ground roasted peanuts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesThickener Context triple: [Kare-Kare, usesThickener, ground roasted peanuts]
-
A.
isThickenedWith
chosen
Indicates that one substance has been made more viscous or dense by adding another substance that serves as a thickening agent.
-
B.
hasThinningProperty
Indicates that one entity possesses a characteristic or effect that reduces the density, thickness, or concentration of another entity.
-
C.
usesSupportMaterial
Indicates that an entity relies on or incorporates additional supporting materials or resources to carry out an action or fulfill a function.
-
D.
hasThicknessRange
Indicates that an entity is associated with a minimum and maximum thickness value defining the range of its thickness.
-
E.
hasMaximumThickness
Indicates that an entity possesses a specified upper limit on its thickness.
- 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_69f224c28c1881908c33b45d689f1724 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f75dc25fa08190b371faf36d9fb72c |
completed | May 3, 2026, 2:37 p.m. |
| PD | Predicate disambiguation | batch_69f758586534819083e91172f4bf5098 |
completed | May 3, 2026, 2:14 p.m. |
Created at: April 29, 2026, 8:54 p.m.