Triple
T11871202
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | dan zai noodles |
E282409
|
entity |
| Predicate | typicalPortionSize |
P67473
|
FINISHED |
| Object | small bowl |
—
|
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: small bowl | Statement: [dan zai noodles, typicalPortionSize, small bowl]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalPortionSize Context triple: [dan zai noodles, typicalPortionSize, small bowl]
-
A.
typicalUnitSize
Indicates the standard or most common size or quantity in which something is typically measured, packaged, or used.
-
B.
servingSizeAtOktoberfest
Indicates the typical quantity or portion in which something (such as food or drink) is served specifically at Oktoberfest.
-
C.
typicalDimension
chosen
Indicates that one entity represents a standard or characteristic measurement (such as size, length, or capacity) typically associated with another entity.
-
D.
typicalFatContent
Indicates the usual or characteristic amount of fat contained in something, such as a food or product.
-
E.
typicalCastSize
Indicates the usual or standard number of cast members involved in a production or performance.
- 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_69d6ab2945d081908a5851c916cbcfb5 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8d39d2934819093b9f7006f45e5cb |
completed | April 10, 2026, 10:40 a.m. |
| PD | Predicate disambiguation | batch_69d8bb272f88819090c37c944c5a60ab |
completed | April 10, 2026, 8:56 a.m. |
Created at: April 8, 2026, 9:43 p.m.