Triple
T1007658
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wayang |
E21749
|
entity |
| Predicate | screenMaterial |
P1272
|
FINISHED |
| Object | cotton screen |
—
|
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: cotton screen | Statement: [Wayang, screenMaterial, cotton screen]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: screenMaterial Context triple: [Wayang, screenMaterial, cotton screen]
-
A.
material
Indicates that one entity is physically composed of, made from, or constructed using the substance or material represented by the other entity.
-
B.
materialUsed
chosen
Indicates that one entity is made from, incorporates, or utilizes the other entity as its material or substance.
-
C.
texture
Indicates the surface quality or feel of an entity as perceived by touch or appearance, such as being smooth, rough, soft, or coarse.
-
D.
surfaceType
Indicates the kind or classification of surface associated with an entity or interaction.
-
E.
sourceMaterialType
Indicates the type or category of material from which something originates or is derived.
- 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_69a493c53e648190ae8cb76c433fd9a7 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b7f4c66c8190b6098fb72c1465a3 |
completed | March 1, 2026, 10:04 p.m. |
| PD | Predicate disambiguation | batch_69a4b7203124819091de68cba5f731c1 |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:41 p.m.