Triple
T4797132
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | focaccia genovese |
E106737
|
entity |
| Predicate | typicalThickness |
P9690
|
FINISHED |
| Object | about 2 centimeters |
—
|
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 2 centimeters | Statement: [focaccia genovese, typicalThickness, about 2 centimeters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalThickness Context triple: [focaccia genovese, typicalThickness, about 2 centimeters]
-
A.
thickness
chosen
Indicates the measure of how deep or wide an object or layer is from one surface or side to its opposite.
-
B.
hasMaximumThickness
Indicates that an entity possesses a specified upper limit on its thickness.
-
C.
typicalLength
Indicates the usual or characteristic length associated with an entity or phenomenon.
-
D.
typicalHeight
Indicates the usual or characteristic height associated with an entity, such as a person, object, or species.
-
E.
typicalWidth
Indicates the usual or characteristic width associated with an entity, as opposed to an exact or measured width in a specific instance.
- 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_69bd43f591c881909e5a532388b0f3f3 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6b40c29c8190adab3503f8ba0145 |
completed | March 20, 2026, 3:44 p.m. |
| PD | Predicate disambiguation | batch_69bd622f88188190a51d52ccfad3d2dd |
completed | March 20, 2026, 3:05 p.m. |
Created at: March 20, 2026, 1:22 p.m.