Triple
T18993989
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apple PCS 7067/8 (UK) |
E464758
|
entity |
| Predicate | languageOfSleeveText |
P48899
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [Apple PCS 7067/8 (UK), languageOfSleeveText, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfSleeveText Context triple: [Apple PCS 7067/8 (UK), languageOfSleeveText, English]
-
A.
hasTypicalSleeveStyle
Indicates the usual or characteristic sleeve design associated with an item, such as a garment or uniform.
-
B.
languageOfMaterial
Indicates the language in which a given material, resource, or content is expressed or presented.
-
C.
languageOfProduct
chosen
Indicates the language in which a product is written, labeled, presented, or otherwise made available.
-
D.
clothingFeature
Indicates that one entity has a specific clothing-related attribute, detail, or characteristic associated with it.
-
E.
textileType
Indicates the specific kind or category of textile material associated with an entity.
- 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_69d8dd01a56c81909694a128c66b21d7 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d67fc90081908f51668620fdc60a |
completed | April 20, 2026, 7:32 a.m. |
| PD | Predicate disambiguation | batch_69e4a2f88e0c81908cb20f08bf24cd32 |
completed | April 19, 2026, 9:40 a.m. |
Created at: April 10, 2026, 12:01 p.m.