Triple
T9051360
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Áo dài |
E216889
|
entity |
| Predicate | typicalFit |
P86094
|
FINISHED |
| Object | form-fitting bodice |
—
|
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: form-fitting bodice | Statement: [Áo dài, typicalFit, form-fitting bodice]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalFit Context triple: [Áo dài, typicalFit, form-fitting bodice]
-
A.
typicalBodyType
Indicates that one entity is the usual or characteristic body type associated with another entity.
-
B.
typicalFabric
Indicates that something is made from or associated with a fabric material that is standard or characteristic for its type.
-
C.
fittedWith
Indicates that one entity is equipped, supplied, or provided with another entity as a component, feature, or accessory.
-
D.
typicalMatchType
Indicates the usual or most common type of match or pairing that characterizes how two entities are related or aligned.
-
E.
typicalIn
Indicates that something commonly occurs, appears, or is found within a given context, category, or environment.
- F. None of above. chosen
Provenance (4 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_69ca83d362e88190ae44b4e4dc194209 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc6b54423081908d9fd985109e336a |
completed | April 1, 2026, 12:48 a.m. |
| PD | Predicate disambiguation | batch_69cc5ee566b081909e3cdaf551dbd0ec |
completed | March 31, 2026, 11:55 p.m. |
| PDg | Predicate description generation | batch_69cc5f4f1cb48190a025d1b3d8d7a790 |
completed | March 31, 2026, 11:57 p.m. |
Created at: March 30, 2026, 7:10 p.m.