Triple
T19825350
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Walla Walla Sweet Onions |
E476308
|
entity |
| Predicate | hasBulbShape |
P82774
|
FINISHED |
| Object | slightly flattened globe |
—
|
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: slightly flattened globe | Statement: [Walla Walla Sweet Onions, hasBulbShape, slightly flattened globe]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBulbShape Context triple: [Walla Walla Sweet Onions, hasBulbShape, slightly flattened globe]
-
A.
hasLanternShape
Indicates that one entity has the form, outline, or configuration characteristic of a lantern.
-
B.
typeOfLampsUsed
Indicates the specific kinds or categories of lamps that are utilized in a given context or system.
-
C.
hasBayShape
Indicates that an entity possesses or exhibits a particular geometric or physical shape characteristic of a bay.
-
D.
hasNumberOfShamashLights
Indicates the relationship specifying how many Shamash (helper) lights are present or associated with an object or setting.
-
E.
hasDistinctiveShape
chosen
Indicates that an entity possesses a shape or form that is notably different from others and can be easily recognized or distinguished.
- 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_69d8e51c7c188190b926f3a2a7b5f881 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e656c9e7348190a569a40bd1fca6ba |
completed | April 20, 2026, 4:39 p.m. |
| PD | Predicate disambiguation | batch_69e5305bda388190a23b7191768107b1 |
completed | April 19, 2026, 7:43 p.m. |
Created at: April 10, 2026, 1:50 p.m.