Triple
T23106621
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yangzhou fried rice |
E576185
|
entity |
| Predicate | typicalProteinComponent |
P150940
|
FINISHED |
| Object | pork |
—
|
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: pork | Statement: [Yangzhou fried rice, typicalProteinComponent, pork]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalProteinComponent Context triple: [Yangzhou fried rice, typicalProteinComponent, pork]
-
A.
typicalProtein
Indicates that one entity is a representative or characteristic example of a particular protein type or class.
-
B.
proteinContent
Indicates the amount or proportion of protein present in a given entity or substance.
-
C.
proteinDomain
Indicates that one entity is a specific structural or functional domain that forms part of the other protein entity.
-
D.
cellularComponent
Indicates the relationship between a biological entity and the specific cellular location or structure in which it is physically present or functions.
-
E.
isProtein
Indicates that the subject entity is classified as a protein.
- F. None of above. chosen
Provenance (4 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_69e245f4af548190898d434a64a1e774 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18e0bb27c8190a17942d9b88bb158 |
completed | April 29, 2026, 4:50 a.m. |
| PD | Predicate disambiguation | batch_69ef89f020588190b43393e048e7eda3 |
completed | April 27, 2026, 4:08 p.m. |
| PDg | Predicate description generation | batch_69ef9b7494f4819088ae59ea3d0ae8ab |
completed | April 27, 2026, 5:23 p.m. |
Created at: April 17, 2026, 3:58 p.m.