Triple
T4307638
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | sulguni cheese |
E93993
|
entity |
| Predicate | hasMoistureContent |
P42283
|
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: [sulguni cheese, hasMoistureContent, high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMoistureContent Context triple: [sulguni cheese, hasMoistureContent, high]
-
A.
receivesMoistureFrom
Indicates that one entity obtains or is supplied with moisture (such as water, humidity, or precipitation) from another entity.
-
B.
wetnessLevel
chosen
Indicates the degree or intensity of how wet something is in relation to a reference state or scale.
-
C.
hasAmyloseContent
Indicates that an entity (typically a food or plant material) possesses a specified amount or proportion of amylose in its starch content.
-
D.
hasSedimentsThat
Indicates that one entity contains, includes, or is associated with specific sediments described by the related entity.
-
E.
hasMineral
Indicates that one entity contains, includes, or is composed of a specified mineral.
- 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_69b3451886588190a3dd1305ea7c58dc |
completed | March 12, 2026, 10:58 p.m. |
| NER | Named-entity recognition | batch_69b350d2af088190ad7cb035d6e0f8c2 |
completed | March 12, 2026, 11:48 p.m. |
| PD | Predicate disambiguation | batch_69b34f4a07b08190a06ada0d9cbb14fb |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:11 p.m.