Triple
T3524670
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chicago SRW wheat |
E74508
|
entity |
| Predicate | hasGrainClass |
P32631
|
FINISHED |
| Object | soft wheat |
—
|
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: soft wheat | Statement: [Chicago SRW wheat, hasGrainClass, soft wheat]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGrainClass Context triple: [Chicago SRW wheat, hasGrainClass, soft wheat]
-
A.
hasGrainType
chosen
Indicates that an entity is characterized by or associated with a specific type of grain.
-
B.
hasGrainQuality
Indicates that an entity possesses a particular level or type of grain quality, characterizing the quality attributes of its grain.
-
C.
hasSpecificGravity
Indicates that one entity has a specific gravity value equal to or characteristic of another entity or reference substance.
-
D.
grain
Indicates that one entity is composed of or contains a granular substance or small particles of another entity.
-
E.
grainSize
Indicates the relative coarseness or fineness of the material or particles involved in the relationship.
- 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_69ad85d0c5488190a3d8e02ebd01a1aa |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbc68b15881909b407486946ec3c5 |
completed | March 8, 2026, 6:14 p.m. |
| PD | Predicate disambiguation | batch_69adae121a048190b03825a001d21f49 |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:19 p.m.