Triple
T11977445
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Triticum aestivum |
E285072
|
entity |
| Predicate | hasProteinContentRange |
P32526
|
FINISHED |
| Object | about 8–15 percent |
—
|
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: about 8–15 percent | Statement: [Triticum aestivum, hasProteinContentRange, about 8–15 percent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProteinContentRange Context triple: [Triticum aestivum, hasProteinContentRange, about 8–15 percent]
-
A.
proteinContent
chosen
Indicates the amount or proportion of protein present in a given entity or substance.
-
B.
hasProteinFeature
Indicates that a protein possesses a specific structural or functional feature, such as a domain, motif, or modification site.
-
C.
mainProtein
Indicates that one protein is the primary or central protein in relation to another entity, such as a complex, pathway, or interaction context.
-
D.
hasCalories
Indicates that an entity contains a specified amount of caloric energy.
-
E.
hasSugarContent
Indicates that one entity possesses or contains a specified amount or level of sugar.
- 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_69d6ab2eaeb881909f7914758f859413 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903acbb9081908fe7f8360057785c |
completed | April 10, 2026, 2:05 p.m. |
| PD | Predicate disambiguation | batch_69d902abca70819098291aa51b593708 |
completed | April 10, 2026, 2:01 p.m. |
Created at: April 8, 2026, 9:46 p.m.