Triple
T9760656
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Morgon |
E236659
|
entity |
| Predicate | typicalFlavours |
P2068
|
FINISHED |
| Object | ripe red fruit |
—
|
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: ripe red fruit | Statement: [Morgon, typicalFlavours, ripe red fruit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalFlavours Context triple: [Morgon, typicalFlavours, ripe red fruit]
-
A.
typicalFlavor
chosen
Indicates that something characteristically has or is associated with a particular flavor.
-
B.
hasVarietyOfFlavors
Indicates that one entity offers or contains multiple distinct flavors or taste options.
-
C.
isOfficialFlavorOf
Indicates that one item is formally recognized or designated as an official flavor associated with another entity (such as a brand, product line, or event).
-
D.
typicalVariety
Indicates that one entity is a representative or characteristic example of the variety or type defined by another entity.
-
E.
typicalSweetnessLevel
Indicates the usual or characteristic degree of sweetness associated with something.
- 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_69ca84d64f6c8190a4ed4e9f5936eda5 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda04ad9008190badfcebe2072ab83 |
completed | April 1, 2026, 10:46 p.m. |
| PD | Predicate disambiguation | batch_69cd03d0772c8190bd1750cf1cfba309 |
completed | April 1, 2026, 11:38 a.m. |
Created at: March 30, 2026, 8:25 p.m.